An online survey with 324 young participants who saw images of the future self was conducted . Structural equation modeling was used to analyze the proposed relationships among psychological continuity , group identification , empathy , sympathy , ageism , psychological proximity , attitudes , and behavioral intentions . Results revealed that psychological continuity created through a vivid image of one 's future self can lead to group identification with the elderly , influencing one 's psychological proximity to issues of the elderly . Conclusions Here we propose a downstream method for the characterization of disease modules from gene expression data . The core of the method is rooted on the identification of significant shortest paths between DEGs by structural equation modeling . This allows to have a mix approach based on data and biological knowledge enclosed in biological pathways . Abstract This study integrated trait , state , and learner perspectives to better understand the potential relationships between self-regulation in L2 writing and two personality factors ( conscientiousness and neuroticism ) in a university EFL context . Based on an innovative mixed methods design , data from cross-sectional and longitudinal samples were analyzed using quantitative and qualitative techniques , including structural equation modeling , visualization tools , and participant interviews . One novel finding of this study was that , from a trait perspective , conscientiousness positively contributed to self-regulation , whereas neuroticism negatively contributed to it . The constructs of optimism subculture and goal setting process are investigated as antecedents to the level of employee optimism and individual and organizational performance are investigated as direct and indirect consequences of the level of employee optimism . Data relating to the constructs were collected from 133 manufacturers and subjected to structural equation modeling analysis . The proposed model fits the data well . Based on the researchs , this paper will analyze the existing methods of the index system ’s effectiveness ----Reliability & Validity Method , Coefficient of Validity Method , Structural Equation Model Method , Signal/Noise Ratio Method ; RST Method , and hope to summarize their advantages , disadvantages and applicable occasions , so as to make the research of the index system effectiveness more perfect . KeywordsEffectiveness of the Index System ; Reliability & Validity Method ; Coefficient of Validity Method ; Structural Equation Model Method ; Signal/Noise Ratio Method ; RST Method . I. INRUODUCTION AND BACKGROUD At present , multiple index comprehensive evaluations have been applied to many fields , such as enterprise management , agriculture , medicine , transportation and so on , which have multiple attribute characteristics , even it can be said that almost all areas are involved in comprehensive evaluation . A structural equation model approach . ABSTRACT Considering that unexpected events are a major contributory factor of road accidents the main objective of this article is to investigate the effect of several parameters including overall driving performance , distraction sources , driver characteristics , as well as road and traffic environment on accident probability at unexpected incidents . In order to make the closed loop supply chain subjects better implement low carbon behavior , we made an analysis of the reliability and validity of the above index scale with statistical software SPSS . Besides , we built an evaluation model of closed loop supply chain subjects’low-carbon behavior with the structural equation model method on the premise of good reliability and validity of the system . The evaluation of low carbon behavior of closed loop supply chain subjects is one of the important contents in this field . Design/methodology/approach The Grey-DEMATEL ( Decision-Making Trial and Evaluation Laboratory ) approach is proposed by the authors to compute the decision results . The present work is supported by the structural modeling equations for supporting sustainability throughout and recognizes the most significant enablers responsible for creating e-waste by replacing the working mobile phones with the new ones . Findings The minimum covariance determinant ( MCD ) estimator of scatter is one of the most famous robust procedures for multivariate scatter . Despite the quite important research activity related to this estimator , culminating in the recent thorough asymptotic study of Cator and Lopuhaa ( 2010 , 2012 ) , no results have been obtained on the corresponding estimator of shape , which is the parameter of interest in many multivariate problems ( including principal component analysis , canonical correlation analysis , testing for sphericity , etc . ) In this paper , we therefore propose and study MCD-based inference procedures for shape , that inherit the good robustness properties of the MCD . This work provides analytical results on the canonical correlation analysis ( CCA ) of data sets from two spatially separated arrays of sensors . Our case studies cover both single source and multiple source signals in either white or colored noise fields for array signal processing . This paper explores the associations between personality traits and performance indicators . CCA ( Canonical Correlation Analysis ) is applied to analyze and optimize the correlations between the two groups of original data , find out the maximum canonical correlation coefficient , convert raw variables into canonical variables . Using sample data , the authors make a detailed explanation for modeling process , showing the dimension reduction effects . The exploratory research reported here appears to be the first to relate comprehensive sets of IT investment measures to organizational strategic and economic performance measures . Although the individual IT investment variables were found to be only weakly related to organizational strategic and economic performance , they were significantly related to performance when grouped and analyzed by means of canonical correlation . More specifically , canonical results suggest that organizational strategic and economic performance measures such as sales by employee , return on sales , sales by total assets , return on investment , and market to book value are affected by IT investment measures such as IT budget as percentage of revenue , the percentage of IT budget spent on training of employees , number of PCs per employee , and IT value as a percentage of revenue . Introduction / Hardware and Software Needs / Univariate Computer Simulations / Basic Correlational Simulations / Multivariate Correlational Simulations / Simulations with Categorical Data / Exploratory Factor Analysis / Confirmatory Factor Analysis and Related Procedures / Regression , Canonical Correlation and Convariance Structure Analysis / Discriminate Analysis , Classification and Multivariate Analysis of Variance / Analysis of Categorical Data Multivariate analysis techniques provide the analyst with additional analysis tools for the examination of relationships , dimension , and structure of large amounts of data . This study examines possible CPE applications for four multivariate analysis techniques : Canonical Correlation , Factor Analysis , Discriminant Analysis , and Cluster Analysis . Also included in the study was the use of ordinary least squares regression modeling and ridge regression modeling , to exemplify the traditional problems encountered with use of regression analysis . For each combination of the selected node similarities , a latent space is generated as a view of the network . A Canonical Correlation Analysis based approach is then used to extract the common structure of the latent spaces alignment , and a neural network based approach is used to extract the view-specific latent structure by measuring the asymmetric KL divergence of nodes ’ Gaussian distribution . The common structure and the view-specific structure of multiple views are merged to perverse the overall network information . We focus on the orientation interaction and color property , and unified those features by calculating the contrast over the entire image resolution . We measure the psychological responses and correspond them to our texture feature based on the canonical correlation statistics . We develop an image retrieval system for textured images based on our model . We have focused on orientation and color features , and unified those features by calculating the contrast over the entire image resolution . Next , we have measured psychological responses and corresponded them to our texture feature based on the canonical correlation statistics . We developed the STIR ( subjective texture image retrieval ) system based on our model . However , classical CCA is unsupervised and does not take class label information into account . In this paper , we introduce the within-class cross correlation into CCA and propose a new method called canonical Random Correlation Analysis ( RCA ) . In RCA , besides considering the correlation between two views from the same sample , the cross correlations between two views respectively from different within-class samples are also used to achieve good performance . However , classical CCA is unsupervised and does not take discriminant information into account . In this paper , we add discriminant information into CCA by using random cross-view correlations between within-class samples and propose a new method for multi-view dimensionality reduction called canonical random correlation analysis ( RCA ) . In RCA , two approaches for randomly generating cross-view correlation samples are developed on the basis of bootstrap technique . Canonical correlation theory is utilized here to modify the set of efficiency measures in data envelopment analysis ( DEA ) . This involves ( 1 ) the mean square error criterion , ( 2 ) the canonical correlation test , and ( 3 ) the set of redundancy tests . An empirical application to educational production frontier data shows the usefulness of these tests and how they can be statistically tested as a two-stage procedure . PART ONE : COLLECTING , PREPARING AND CHECKING THE DATA Measurement , Errors and Data for Consumer Research Secondary Consumer Data Primary Data Collection Data Preparation and Descriptive Statistics PART TWO : SAMPLING , PROBABILITY AND INFERENCE Sampling Hypothesis Testing Analysis of Variance PART THREE : RELATIONSHIPS AMONG VARIABLES Correlation and Regression Association , Log-linear Analysis and Canonical Correlation Analysis Factor Analysis and Principal Component Analysis PART FOUR : CLASSIFICATION AND SEGMENTATION TECHNIQUES Discriminant Analysis Cluster Analysis Multidimensional Scaling Correspondence Analysis PART FIVE : FURTHER METHODS IN MULTIVARIATE ANALYSIS Structural Equation Models Discrete Choice Models The End ( and Beyond ) This study examines how young consumers perceive Internet banking in relation to other six banking channels ( brick and mortar , automated teller machine ( ATM ) , phone banking , wireless application protocol ( WAP ) , electronic fund transfer at point of sale ( EFTPOS ) , and bank branches in stores ) . Correspondence analysis and cluster analysis revealed the banking channels that are close with Internet banking . The results indicate that Internet banking , ATM , and phone banking substitute each other . We then utilize graphical correspondence analysis to exploring the implementation of OVOP programs and conduct some meaningful suggestions of the policy direction from these empirical cases . Through this study we successfully demonstrate that correspondence analysis is an efficiency technique for industrial analysis and strategy management in real world . Visualization and Verbalization of Data shows how correspondence analysis and related techniques enable the display of data in graphical form , which results in the verbalization of the structures in data . Renowned researchers in the field trace the history of these techniques and cover their current applications . In addition , the method also identifies genes that display similar or comparable time-course expression patterns shared by both the cases and the controls . We conclude that our correspondence analysis based approach can be a useful tool for analyzing high dimensional microarray data collected in clinical investigations . The data collection was easily implemented with free software tools to facilitate its use in low-income urban territories . The method combines the evaluation of experts using the of the traditional approach for the qualification of the variables of vulnerability in its three components ( exposure , susceptibility , and resilience ) , and incorporates a computational method of the correspondence analysis family to formulate the indicators of vulnerability . The results showed that the multiple correspondence analysis is useful for the identification of the most representative variables in the vulnerability assessment , used for the construction of spatial disaggregated vulnerability indicators and therefore the development of vulnerability maps that will help in the short term in disaster risk management , urban planning , and infrastructure protection . In the indefinite system analysis , there is much man-made plasticity for disposition counterpoise value and mathematical value ranges . Thus , this paper attempts to apply correspondence analysis method model to approach the reliability which to be used to evaluate the ground water quality system for dividing districts and classification . Aimed at the security certification system of power system information exchange based on the signature system of the digital certificate , the PCI cryptographic card and the embedded cryptographic computing module with ARM as its core are designed . Through the embedded cryptographic computing module or PCI cryptographic card , the security authentication of communication between the existing substation equipment and each information exchange subject is implemented . One way to deal with this metamorphic malware behavior is the use of dependency graphs , generated by surveying dependency relationships among code elements , creating a model that is resilient to code mutations . Analog to the signature model , a matching procedure that compares these graphs with a reference graph database is used to identify a malware code . Since graph matching is a NP-hard problem , it is necessary to find ways to optimize this process , so this identification technique can be applied . To improve the efficiency , a new identity-based cryptography threshold group signature scheme was proposed basing on bilinear pairing and secret sharing . In this scheme , the master key was distributed into the whole group and the user 's signature key was stored in a threshold way , these methods ensured the whole security of signature systems . The scheme with a identity-based t-out-of-n secret sharing algorithm was highly-efficient . A random queries scheme , which is based on opportunistic target signature propagation , sends query messages in random chosen directions to hit a sensor node that contains the given target signature near the query node . However , queries for targets whose signature propagation is very limited need a large number of random query messages to achieve high query reliability . Our algorithm combines the two query schemes . Our solution allows selecting and revoking the users based on the attributes . We propose three algorithms , namely , Secured Encryption using Group-Based Signature Generation ( SEGB-SG ) , Secure Verification using Group-Based Signature Verification ( SVGB-SV ) , and Revocation List based on Group Signature ( RL-GS ) . The results for the proposed solution show a significant improvement in providing security for data sharing in the cloud . We present a construction for hash-based one-time group signature schemes , and develop a traceable post-quantum multi-time group signature upon it . A group signature scheme allows group members to anonymously sign a message on behalf of the whole group . We describe a novel key distribution and management scheme for clustered ad hoc sensor networks . The scheme uses the Boneh-Franklin ’s ID-based encryption ( IBE ) scheme and Yi ’s ID-based signature scheme to achieve mutual authentication between nodes . The signature scheme is used to distribute a cluster key which can be updated . This paper proposes a novel ID-based aggregate proxy signature scheme that realizes a warrant-based delegation for an original signer to transfer his/her signing power to a given set of proxy signers . Our proposed scheme allows n distinct proxy signers to sign n distinct messages in such a way that these n individual signatures can be aggregated into a single one without expansion . To solve the corresponding problems , some possible methods are discussed . The conclusion is that with the help of the Chameleon function , the proxy signature can be made shorter and easily calculated;the security model of hieratical authorization signature can be established based on the security model of sequential multi-signature;knowledge proof method can be used in designing the revocation scheme of signature . A verifier verifies only both signer 's signature using self-certified signatures , but it verifies over two certifications to certification chain in cross-certification , it ca n't be satisfied with demand in actual application . To the above problem , a new digital signature scheme called multi-certification signature was proposed . The method extents self-certified signatures and verifies three certifications once and extents more multi-certification signature which can verifies multi-signatures once . This is followed by a calculation of the graph nodes ’ metrics , which are used in the selection from the original graph a subset of nodes to be monitored . The paper is organised as follows : it will outline the problem definition and algorithm , then will proceed to a brief description of an event and signature based model used to instrument monitored nodes . Finally , the paper will conclude with an evaluation using an infection detection scenario , which will be followed by a general discussion and proposed further work . This paper describes the initial phase of an evaluation study on the performance of PMO , the Paint Map Optimizer , for long wave infrared ( LWIR ) modeling . In this phase , we will evaluate using PRIMS , the Physically Reasonable Infrared Signature Modeler , to predict the thermal signature of a simplified tank geometry , and then PMO to predict the optimal thermal camouflage pattern from a range of emissivities in a given scenario . Prism is a thermal modeling code that has been used extensively to model thermal signatures of military ground vehicles . Simulations studies are used to theoretically examine mean square error performance and to explore the benefit of voxel-level analysis when the primary interest is a statistical summary of regional kinetics . The work highlights the contribution that multivariate analysis tools and life-table concepts can make in the recovery of local metabolic information from dynamic PET studies , particularly ones in which the assumptions of compartmental-like models , with residues that are sums of exponentials , might not be certain . Three identical 5-h continuous manufacturing runs were performed . Multivariate data analysis tools , more specifically latent variable modeling tools such as principal component analysis , were used to extract information from the generated data sets unveiling process trends and drifts . Furthermore , a statistical process monitoring strategy is presented . Analysis of leaf reflectance information from digitized leaf images allows high-throughput , non-invasive and real-time estimation of chlorophyll content in a cost-effective manner . In the present study the application of multivariate data analysis tools , viz . principal component analysis ( PCA ) and agglomerative hierarchical clustering analysis ( AHCA ) , has been discussed for distinguishing between spinach seedlings having high and low chlorophyll contents by simultaneously using the information provided by various image features . This is opposed to off-line or at-line methods that lead to sampling problems , time delays , and in general , an inability to control and optimize chemical processes . The Center for Process Analytical Chemistry ( CPAC ) at the University of Washington has long recognized that the preferred approach to process analysis is non-invasive and has pioneered a variety of process analyzers and associated multivariate data analysis tools to exploit the relatively few physical and chemical phenomena available to the non-invasive approach . This work is similar to research at the Center for Non-Destructive Evaluation ( NDE ) except that the CPAC emphasis is on quantitative chemical information . The author investigated the big data papers included China Academic Journal Network Publishing Database of CNKI from 2002 to 2012 , with the help of multivariate statistical analysis tools SPSS17.0 of cluster analysis , multidimensional scaling analysis and visualization tools Ucinet6 from posting growth law , distribution of authors , research institutes distribution , periodical distribution , and high-frequency words such as multiple perspectives bibliometrical analysis and visualization analysis , the data reveal a large field of research status , and summarizes research characteristics , in order to study big data in-depth development and the future development of big data provide reference information . A multivariate statistical theory , local feature analysis ( LFA ) , extracts functionally relevant domains from molecular dynamics ( MD ) trajectories . The LFA representations , like those of principal component analysis ( PCA ) , are low dimensional and provide a reduced basis set for collective motions of simulated proteins , but the local features are sparsely distributed and spatially localized , in contrast to global PCA modes . Reflectance spectra of living wheat ’s leaves were acquired by portable radiation spectrometer . Multivariate scatter correction ( MSC ) method was used to preprocess the spectra , and quantitative analysis detection models were built up by artificial neural networks ( ANN ) . The models of chlorophyll , chlorophyll a , and chlorophyll b in leaves were built up and tested . Combined with the results of continuous section HE staining , the FTIR-MSP spectral data of the corresponding skin tissues were acquired . With the combination of machine learning algorithms such as principal component analysis （ PCA ） and partial least squares-discriminant analysis （ PLS-DA ） , different spectral bands were selected （ full band 4 000 - 1 000 cm-1 and sub-bands 4 000 - 3 600 cm-1 , 3 600 - 2 800 cm-1 , 2 800 - 1 800 cm-1 , and 1 800 - 1 000 cm-1 ） , and various pretreatment methods were used such as orthogonal signal correction （ OSC ） , standard normal variables （ SNV ） , multivariate scatter correction （ MSC ） , normalization , and smoothing . Thus , the model was optimized , and the classification effects were compared . In this study , a hyperspectral imaging system of 866.4–1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface . In order to obtain the optimal model combination , three preprocessing methods ( standard normal variate ( SNV ) , Savitzky – Golay first derivative ( SG1 ) , and multivariate scatter correction ( MSC ) ) , three feature extraction algorithms ( successive projections algorithm ( SPA ) , random frog ( RF ) , and neighborhood component analysis ( NCA ) ) , and three classifier models ( decision tree ( DT ) , k-nearest neighbor ( KNN ) , and support vector machine ( SVM ) ) were applied to make a comparison . Firstly , based on the full wavelengths modeling analysis , it was found that the spectral data after MSC processing performed best in the three classifier models . Results . Factorized time multivariate CPPs make it possible to use methods of multivariate statistical analysis for evaluating the influence of CPPs factors on the time multivariate CQAs . Conclusions . Preparatory transformation of clusters of critical process ( manufacture process ) parameters into factors of product critical quality attributes was carried out . Factorized time multivariate CPPs enable using the methods of multivariate statistical analysis for assessing the impact of CPP factors on the time multivariate CQAs . Conclusions . The remote sensing ( RS ) technique is less cost- and labour- intensive than ground-based surveys for diverse applications in agriculture . Machine learning ( ML ) , a branch of artificial intelligence ( AI ) , provides an effective approach to construct a model for regression and classification of a multivariate and non-linear system . Without being explicitly programmed , machine learning models learn from training data , i.e. , past experience . To manage interest rate risk effectively , it would be desirable to have an idea of how the term structure changes over time . Techniques ranging from econometric modelling , time series analysis , including multivariate and non-linear systems , as well as neural networks and pattern recognition are used for forecasting purposes . However , for pricing derivative instruments the theory of diffusion processes and stochastic calculus are employed . In many privacy preserving applications , specific variables are required to be disturbed simultaneously in order to guarantee correlations among them . Multivariate Equi-Depth Swapping ( MEDS ) is a natural solution in such cases , since it provides uniform privacy protection for each data tuple . However , this approach performs ineffectively not only in computational complexity ( basically O(n 3 ) for n data tuples ) , but in data utility for distance-based data analysis . In this paper , we study several issues related to the characterization of specific classes of multivariate quadratic mappings that are nonnegative over a given domain , with nonnegativity defined by a prespecified conic order . In particular , we consider the set ( cone ) of nonnegative quadratic mappings , defined with respect to the positive semidefinite matrix cone , and study when it can be represented by linear matrix inequalities . This paper investigates the filtering problem for multivariate continuous nonlinear non-Gaussian systems based on an improved minimum error entropy ( MEE ) criterion . The system is described by a set of nonlinear continuous equations with non-Gaussian system noises and measurement noises . We present a low-complexity model-based FOPEN target detection algorithm and discuss its potential application as a target screener within an end-to-end FOPEN SAR automatic target detection system . The algorithm uses multiple discriminants extracted over a local sliding window followed by a multivariate discrimination rule to perform target screening at the pixel level . We present detection performance results obtained against FOPEN SAR imagery and show that the multidiscriminant approach achieves better detection performance than a model-template matched-filter detection algorithm . So far , in applications of RR methods it has been assumed that they eliminate response biases . We investigate the validity of this assumption by applying multivariate RR models that allow for different types of response biases . Our data analyses show that RR methods do not eliminate response biases but that they can be modeled in informative ways : accounting for response biases leads to estimates that are at least twice the size of the estimates obtained when response biases are ignored . This paper proposes a real-time virtual machine ( VM ) provisioning framework to minimize energy consumption in a cloud data center with an efficient and novel data pre-processing , real-time clustering , instant multivariate time-series prediction and placement algorithms . The proposed framework represents an enhanced version of our recent work and it is suitable for real-time VM consolidation as it takes into consideration user behavior and VM historical usage to forecast the number of future VMs and to improve consolidation process with minimum energy consumption .
An online survey with 324 young participants who saw images of the future self was conducted . Structural equation modeling was used to analyze the proposed relationships among psychological continuity , group identification , empathy , sympathy , ageism , psychological proximity , attitudes , and behavioral intentions . Results revealed that psychological continuity created through a vivid image of one 's future self can lead to group identification with the elderly , influencing one 's psychological proximity to issues of the elderly .
Conclusions Here we propose a downstream method for the characterization of disease modules from gene expression data . The core of the method is rooted on the identification of significant shortest paths between DEGs by structural equation modeling . This allows to have a mix approach based on data and biological knowledge enclosed in biological pathways .
Abstract This study integrated trait , state , and learner perspectives to better understand the potential relationships between self-regulation in L2 writing and two personality factors ( conscientiousness and neuroticism ) in a university EFL context . Based on an innovative mixed methods design , data from cross-sectional and longitudinal samples were analyzed using quantitative and qualitative techniques , including structural equation modeling , visualization tools , and participant interviews . One novel finding of this study was that , from a trait perspective , conscientiousness positively contributed to self-regulation , whereas neuroticism negatively contributed to it .
The constructs of optimism subculture and goal setting process are investigated as antecedents to the level of employee optimism and individual and organizational performance are investigated as direct and indirect consequences of the level of employee optimism . Data relating to the constructs were collected from 133 manufacturers and subjected to structural equation modeling analysis . The proposed model fits the data well .
Based on the researchs , this paper will analyze the existing methods of the index system ’s effectiveness ----Reliability & Validity Method , Coefficient of Validity Method , Structural Equation Model Method , Signal/Noise Ratio Method ; RST Method , and hope to summarize their advantages , disadvantages and applicable occasions , so as to make the research of the index system effectiveness more perfect . KeywordsEffectiveness of the Index System ; Reliability & Validity Method ; Coefficient of Validity Method ; Structural Equation Model Method ; Signal/Noise Ratio Method ; RST Method . I. INRUODUCTION AND BACKGROUD At present , multiple index comprehensive evaluations have been applied to many fields , such as enterprise management , agriculture , medicine , transportation and so on , which have multiple attribute characteristics , even it can be said that almost all areas are involved in comprehensive evaluation .
A structural equation model approach . ABSTRACT Considering that unexpected events are a major contributory factor of road accidents the main objective of this article is to investigate the effect of several parameters including overall driving performance , distraction sources , driver characteristics , as well as road and traffic environment on accident probability at unexpected incidents .
In order to make the closed loop supply chain subjects better implement low carbon behavior , we made an analysis of the reliability and validity of the above index scale with statistical software SPSS . Besides , we built an evaluation model of closed loop supply chain subjects’low-carbon behavior with the structural equation model method on the premise of good reliability and validity of the system . The evaluation of low carbon behavior of closed loop supply chain subjects is one of the important contents in this field .
Design/methodology/approach The Grey-DEMATEL ( Decision-Making Trial and Evaluation Laboratory ) approach is proposed by the authors to compute the decision results . The present work is supported by the structural modeling equations for supporting sustainability throughout and recognizes the most significant enablers responsible for creating e-waste by replacing the working mobile phones with the new ones . Findings
The minimum covariance determinant ( MCD ) estimator of scatter is one of the most famous robust procedures for multivariate scatter . Despite the quite important research activity related to this estimator , culminating in the recent thorough asymptotic study of Cator and Lopuhaa ( 2010 , 2012 ) , no results have been obtained on the corresponding estimator of shape , which is the parameter of interest in many multivariate problems ( including principal component analysis , canonical correlation analysis , testing for sphericity , etc . ) In this paper , we therefore propose and study MCD-based inference procedures for shape , that inherit the good robustness properties of the MCD .
This work provides analytical results on the canonical correlation analysis ( CCA ) of data sets from two spatially separated arrays of sensors . Our case studies cover both single source and multiple source signals in either white or colored noise fields for array signal processing .
This paper explores the associations between personality traits and performance indicators . CCA ( Canonical Correlation Analysis ) is applied to analyze and optimize the correlations between the two groups of original data , find out the maximum canonical correlation coefficient , convert raw variables into canonical variables . Using sample data , the authors make a detailed explanation for modeling process , showing the dimension reduction effects .
The exploratory research reported here appears to be the first to relate comprehensive sets of IT investment measures to organizational strategic and economic performance measures . Although the individual IT investment variables were found to be only weakly related to organizational strategic and economic performance , they were significantly related to performance when grouped and analyzed by means of canonical correlation . More specifically , canonical results suggest that organizational strategic and economic performance measures such as sales by employee , return on sales , sales by total assets , return on investment , and market to book value are affected by IT investment measures such as IT budget as percentage of revenue , the percentage of IT budget spent on training of employees , number of PCs per employee , and IT value as a percentage of revenue .
Introduction / Hardware and Software Needs / Univariate Computer Simulations / Basic Correlational Simulations / Multivariate Correlational Simulations / Simulations with Categorical Data / Exploratory Factor Analysis / Confirmatory Factor Analysis and Related Procedures / Regression , Canonical Correlation and Convariance Structure Analysis / Discriminate Analysis , Classification and Multivariate Analysis of Variance / Analysis of Categorical Data
Multivariate analysis techniques provide the analyst with additional analysis tools for the examination of relationships , dimension , and structure of large amounts of data . This study examines possible CPE applications for four multivariate analysis techniques : Canonical Correlation , Factor Analysis , Discriminant Analysis , and Cluster Analysis . Also included in the study was the use of ordinary least squares regression modeling and ridge regression modeling , to exemplify the traditional problems encountered with use of regression analysis .
For each combination of the selected node similarities , a latent space is generated as a view of the network . A Canonical Correlation Analysis based approach is then used to extract the common structure of the latent spaces alignment , and a neural network based approach is used to extract the view-specific latent structure by measuring the asymmetric KL divergence of nodes ’ Gaussian distribution . The common structure and the view-specific structure of multiple views are merged to perverse the overall network information .
We focus on the orientation interaction and color property , and unified those features by calculating the contrast over the entire image resolution . We measure the psychological responses and correspond them to our texture feature based on the canonical correlation statistics . We develop an image retrieval system for textured images based on our model .
We have focused on orientation and color features , and unified those features by calculating the contrast over the entire image resolution . Next , we have measured psychological responses and corresponded them to our texture feature based on the canonical correlation statistics . We developed the STIR ( subjective texture image retrieval ) system based on our model .
However , classical CCA is unsupervised and does not take class label information into account . In this paper , we introduce the within-class cross correlation into CCA and propose a new method called canonical Random Correlation Analysis ( RCA ) . In RCA , besides considering the correlation between two views from the same sample , the cross correlations between two views respectively from different within-class samples are also used to achieve good performance .
However , classical CCA is unsupervised and does not take discriminant information into account . In this paper , we add discriminant information into CCA by using random cross-view correlations between within-class samples and propose a new method for multi-view dimensionality reduction called canonical random correlation analysis ( RCA ) . In RCA , two approaches for randomly generating cross-view correlation samples are developed on the basis of bootstrap technique .
Canonical correlation theory is utilized here to modify the set of efficiency measures in data envelopment analysis ( DEA ) . This involves ( 1 ) the mean square error criterion , ( 2 ) the canonical correlation test , and ( 3 ) the set of redundancy tests . An empirical application to educational production frontier data shows the usefulness of these tests and how they can be statistically tested as a two-stage procedure .
PART ONE : COLLECTING , PREPARING AND CHECKING THE DATA Measurement , Errors and Data for Consumer Research Secondary Consumer Data Primary Data Collection Data Preparation and Descriptive Statistics PART TWO : SAMPLING , PROBABILITY AND INFERENCE Sampling Hypothesis Testing Analysis of Variance PART THREE : RELATIONSHIPS AMONG VARIABLES Correlation and Regression Association , Log-linear Analysis and Canonical Correlation Analysis Factor Analysis and Principal Component Analysis PART FOUR : CLASSIFICATION AND SEGMENTATION TECHNIQUES Discriminant Analysis Cluster Analysis Multidimensional Scaling Correspondence Analysis PART FIVE : FURTHER METHODS IN MULTIVARIATE ANALYSIS Structural Equation Models Discrete Choice Models The End ( and Beyond )
This study examines how young consumers perceive Internet banking in relation to other six banking channels ( brick and mortar , automated teller machine ( ATM ) , phone banking , wireless application protocol ( WAP ) , electronic fund transfer at point of sale ( EFTPOS ) , and bank branches in stores ) . Correspondence analysis and cluster analysis revealed the banking channels that are close with Internet banking . The results indicate that Internet banking , ATM , and phone banking substitute each other .
We then utilize graphical correspondence analysis to exploring the implementation of OVOP programs and conduct some meaningful suggestions of the policy direction from these empirical cases . Through this study we successfully demonstrate that correspondence analysis is an efficiency technique for industrial analysis and strategy management in real world .
Visualization and Verbalization of Data shows how correspondence analysis and related techniques enable the display of data in graphical form , which results in the verbalization of the structures in data . Renowned researchers in the field trace the history of these techniques and cover their current applications .
In addition , the method also identifies genes that display similar or comparable time-course expression patterns shared by both the cases and the controls . We conclude that our correspondence analysis based approach can be a useful tool for analyzing high dimensional microarray data collected in clinical investigations .
The data collection was easily implemented with free software tools to facilitate its use in low-income urban territories . The method combines the evaluation of experts using the of the traditional approach for the qualification of the variables of vulnerability in its three components ( exposure , susceptibility , and resilience ) , and incorporates a computational method of the correspondence analysis family to formulate the indicators of vulnerability . The results showed that the multiple correspondence analysis is useful for the identification of the most representative variables in the vulnerability assessment , used for the construction of spatial disaggregated vulnerability indicators and therefore the development of vulnerability maps that will help in the short term in disaster risk management , urban planning , and infrastructure protection .
In the indefinite system analysis , there is much man-made plasticity for disposition counterpoise value and mathematical value ranges . Thus , this paper attempts to apply correspondence analysis method model to approach the reliability which to be used to evaluate the ground water quality system for dividing districts and classification .
Aimed at the security certification system of power system information exchange based on the signature system of the digital certificate , the PCI cryptographic card and the embedded cryptographic computing module with ARM as its core are designed . Through the embedded cryptographic computing module or PCI cryptographic card , the security authentication of communication between the existing substation equipment and each information exchange subject is implemented .
One way to deal with this metamorphic malware behavior is the use of dependency graphs , generated by surveying dependency relationships among code elements , creating a model that is resilient to code mutations . Analog to the signature model , a matching procedure that compares these graphs with a reference graph database is used to identify a malware code . Since graph matching is a NP-hard problem , it is necessary to find ways to optimize this process , so this identification technique can be applied .
To improve the efficiency , a new identity-based cryptography threshold group signature scheme was proposed basing on bilinear pairing and secret sharing . In this scheme , the master key was distributed into the whole group and the user 's signature key was stored in a threshold way , these methods ensured the whole security of signature systems . The scheme with a identity-based t-out-of-n secret sharing algorithm was highly-efficient .
A random queries scheme , which is based on opportunistic target signature propagation , sends query messages in random chosen directions to hit a sensor node that contains the given target signature near the query node . However , queries for targets whose signature propagation is very limited need a large number of random query messages to achieve high query reliability . Our algorithm combines the two query schemes .
Our solution allows selecting and revoking the users based on the attributes . We propose three algorithms , namely , Secured Encryption using Group-Based Signature Generation ( SEGB-SG ) , Secure Verification using Group-Based Signature Verification ( SVGB-SV ) , and Revocation List based on Group Signature ( RL-GS ) . The results for the proposed solution show a significant improvement in providing security for data sharing in the cloud .
We present a construction for hash-based one-time group signature schemes , and develop a traceable post-quantum multi-time group signature upon it . A group signature scheme allows group members to anonymously sign a message on behalf of the whole group .
We describe a novel key distribution and management scheme for clustered ad hoc sensor networks . The scheme uses the Boneh-Franklin ’s ID-based encryption ( IBE ) scheme and Yi ’s ID-based signature scheme to achieve mutual authentication between nodes . The signature scheme is used to distribute a cluster key which can be updated .
This paper proposes a novel ID-based aggregate proxy signature scheme that realizes a warrant-based delegation for an original signer to transfer his/her signing power to a given set of proxy signers . Our proposed scheme allows n distinct proxy signers to sign n distinct messages in such a way that these n individual signatures can be aggregated into a single one without expansion .
To solve the corresponding problems , some possible methods are discussed . The conclusion is that with the help of the Chameleon function , the proxy signature can be made shorter and easily calculated;the security model of hieratical authorization signature can be established based on the security model of sequential multi-signature;knowledge proof method can be used in designing the revocation scheme of signature .
A verifier verifies only both signer 's signature using self-certified signatures , but it verifies over two certifications to certification chain in cross-certification , it ca n't be satisfied with demand in actual application . To the above problem , a new digital signature scheme called multi-certification signature was proposed . The method extents self-certified signatures and verifies three certifications once and extents more multi-certification signature which can verifies multi-signatures once .
This is followed by a calculation of the graph nodes ’ metrics , which are used in the selection from the original graph a subset of nodes to be monitored . The paper is organised as follows : it will outline the problem definition and algorithm , then will proceed to a brief description of an event and signature based model used to instrument monitored nodes . Finally , the paper will conclude with an evaluation using an infection detection scenario , which will be followed by a general discussion and proposed further work .
This paper describes the initial phase of an evaluation study on the performance of PMO , the Paint Map Optimizer , for long wave infrared ( LWIR ) modeling . In this phase , we will evaluate using PRIMS , the Physically Reasonable Infrared Signature Modeler , to predict the thermal signature of a simplified tank geometry , and then PMO to predict the optimal thermal camouflage pattern from a range of emissivities in a given scenario . Prism is a thermal modeling code that has been used extensively to model thermal signatures of military ground vehicles .
Simulations studies are used to theoretically examine mean square error performance and to explore the benefit of voxel-level analysis when the primary interest is a statistical summary of regional kinetics . The work highlights the contribution that multivariate analysis tools and life-table concepts can make in the recovery of local metabolic information from dynamic PET studies , particularly ones in which the assumptions of compartmental-like models , with residues that are sums of exponentials , might not be certain .
Three identical 5-h continuous manufacturing runs were performed . Multivariate data analysis tools , more specifically latent variable modeling tools such as principal component analysis , were used to extract information from the generated data sets unveiling process trends and drifts . Furthermore , a statistical process monitoring strategy is presented .
Analysis of leaf reflectance information from digitized leaf images allows high-throughput , non-invasive and real-time estimation of chlorophyll content in a cost-effective manner . In the present study the application of multivariate data analysis tools , viz . principal component analysis ( PCA ) and agglomerative hierarchical clustering analysis ( AHCA ) , has been discussed for distinguishing between spinach seedlings having high and low chlorophyll contents by simultaneously using the information provided by various image features .
This is opposed to off-line or at-line methods that lead to sampling problems , time delays , and in general , an inability to control and optimize chemical processes . The Center for Process Analytical Chemistry ( CPAC ) at the University of Washington has long recognized that the preferred approach to process analysis is non-invasive and has pioneered a variety of process analyzers and associated multivariate data analysis tools to exploit the relatively few physical and chemical phenomena available to the non-invasive approach . This work is similar to research at the Center for Non-Destructive Evaluation ( NDE ) except that the CPAC emphasis is on quantitative chemical information .
The author investigated the big data papers included China Academic Journal Network Publishing Database of CNKI from 2002 to 2012 , with the help of multivariate statistical analysis tools SPSS17.0 of cluster analysis , multidimensional scaling analysis and visualization tools Ucinet6 from posting growth law , distribution of authors , research institutes distribution , periodical distribution , and high-frequency words such as multiple perspectives bibliometrical analysis and visualization analysis , the data reveal a large field of research status , and summarizes research characteristics , in order to study big data in-depth development and the future development of big data provide reference information .
A multivariate statistical theory , local feature analysis ( LFA ) , extracts functionally relevant domains from molecular dynamics ( MD ) trajectories . The LFA representations , like those of principal component analysis ( PCA ) , are low dimensional and provide a reduced basis set for collective motions of simulated proteins , but the local features are sparsely distributed and spatially localized , in contrast to global PCA modes .
Reflectance spectra of living wheat ’s leaves were acquired by portable radiation spectrometer . Multivariate scatter correction ( MSC ) method was used to preprocess the spectra , and quantitative analysis detection models were built up by artificial neural networks ( ANN ) . The models of chlorophyll , chlorophyll a , and chlorophyll b in leaves were built up and tested .
Combined with the results of continuous section HE staining , the FTIR-MSP spectral data of the corresponding skin tissues were acquired . With the combination of machine learning algorithms such as principal component analysis （ PCA ） and partial least squares-discriminant analysis （ PLS-DA ） , different spectral bands were selected （ full band 4 000 - 1 000 cm-1 and sub-bands 4 000 - 3 600 cm-1 , 3 600 - 2 800 cm-1 , 2 800 - 1 800 cm-1 , and 1 800 - 1 000 cm-1 ） , and various pretreatment methods were used such as orthogonal signal correction （ OSC ） , standard normal variables （ SNV ） , multivariate scatter correction （ MSC ） , normalization , and smoothing . Thus , the model was optimized , and the classification effects were compared .
In this study , a hyperspectral imaging system of 866.4–1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface . In order to obtain the optimal model combination , three preprocessing methods ( standard normal variate ( SNV ) , Savitzky – Golay first derivative ( SG1 ) , and multivariate scatter correction ( MSC ) ) , three feature extraction algorithms ( successive projections algorithm ( SPA ) , random frog ( RF ) , and neighborhood component analysis ( NCA ) ) , and three classifier models ( decision tree ( DT ) , k-nearest neighbor ( KNN ) , and support vector machine ( SVM ) ) were applied to make a comparison . Firstly , based on the full wavelengths modeling analysis , it was found that the spectral data after MSC processing performed best in the three classifier models .
Results . Factorized time multivariate CPPs make it possible to use methods of multivariate statistical analysis for evaluating the influence of CPPs factors on the time multivariate CQAs . Conclusions .
Preparatory transformation of clusters of critical process ( manufacture process ) parameters into factors of product critical quality attributes was carried out . Factorized time multivariate CPPs enable using the methods of multivariate statistical analysis for assessing the impact of CPP factors on the time multivariate CQAs . Conclusions .
The remote sensing ( RS ) technique is less cost- and labour- intensive than ground-based surveys for diverse applications in agriculture . Machine learning ( ML ) , a branch of artificial intelligence ( AI ) , provides an effective approach to construct a model for regression and classification of a multivariate and non-linear system . Without being explicitly programmed , machine learning models learn from training data , i.e. , past experience .
To manage interest rate risk effectively , it would be desirable to have an idea of how the term structure changes over time . Techniques ranging from econometric modelling , time series analysis , including multivariate and non-linear systems , as well as neural networks and pattern recognition are used for forecasting purposes . However , for pricing derivative instruments the theory of diffusion processes and stochastic calculus are employed .
In many privacy preserving applications , specific variables are required to be disturbed simultaneously in order to guarantee correlations among them . Multivariate Equi-Depth Swapping ( MEDS ) is a natural solution in such cases , since it provides uniform privacy protection for each data tuple . However , this approach performs ineffectively not only in computational complexity ( basically O(n 3 ) for n data tuples ) , but in data utility for distance-based data analysis .
In this paper , we study several issues related to the characterization of specific classes of multivariate quadratic mappings that are nonnegative over a given domain , with nonnegativity defined by a prespecified conic order . In particular , we consider the set ( cone ) of nonnegative quadratic mappings , defined with respect to the positive semidefinite matrix cone , and study when it can be represented by linear matrix inequalities .
This paper investigates the filtering problem for multivariate continuous nonlinear non-Gaussian systems based on an improved minimum error entropy ( MEE ) criterion . The system is described by a set of nonlinear continuous equations with non-Gaussian system noises and measurement noises .
We present a low-complexity model-based FOPEN target detection algorithm and discuss its potential application as a target screener within an end-to-end FOPEN SAR automatic target detection system . The algorithm uses multiple discriminants extracted over a local sliding window followed by a multivariate discrimination rule to perform target screening at the pixel level . We present detection performance results obtained against FOPEN SAR imagery and show that the multidiscriminant approach achieves better detection performance than a model-template matched-filter detection algorithm .
So far , in applications of RR methods it has been assumed that they eliminate response biases . We investigate the validity of this assumption by applying multivariate RR models that allow for different types of response biases . Our data analyses show that RR methods do not eliminate response biases but that they can be modeled in informative ways : accounting for response biases leads to estimates that are at least twice the size of the estimates obtained when response biases are ignored .
This paper proposes a real-time virtual machine ( VM ) provisioning framework to minimize energy consumption in a cloud data center with an efficient and novel data pre-processing , real-time clustering , instant multivariate time-series prediction and placement algorithms . The proposed framework represents an enhanced version of our recent work and it is suitable for real-time VM consolidation as it takes into consideration user behavior and VM historical usage to forecast the number of future VMs and to improve consolidation process with minimum energy consumption .
Psychological proximity to issues of the elderly: The role of age-morphing technology in campaigns for the elderly Significant Shortest Paths For The Detection Of Putative Disease Modules Self-regulation and personality among L2 writers: Integrating trait, state, and learner perspectives Developing optimism to improve performance: an approach for the manufacturing sector Review on Methods of Comprehensive Evaluation Index System’s Effectiveness Which factors affect accident probability at unexpected incidents? A structural equation model approach Research on the Influence Factors of Closed-Loop Supply Chain Subjects’Low-Carbon Behavior A Grey-DEMATEL approach for implicating e-waste management practice: Modeling in context of Indian scenario Inference on the shape of elliptical distributions based on the MCD Does canonical correlation analysis provide reliable information on data correlation in array processing? Research on relationships between potential of marketing staff and their job performance Measuring the Organizational Impact of Information Technology Investment: An Exploratory Study Statistical Data Analysis Using Your Personal Computer Applications of Multivariate Statistical Techniques for Computer Performance Evaluation. Multi-view network embedding with node similarity ensemble Multi-contrast based texture model for understanding human subjectivity The stir system: subjective texture image retrieval system Canonical random correlation analysis Multi-view dimensionality reduction via canonical random correlation analysis Tests of efficiency in data envelopment analysis Statistics for Marketing and Consumer Research Internet banking versus other banking channels: Young consumers' view Correspondence Analysis to Screening the Implementation of One Village One Product Programs in Taiwan History of cluster analysis Correspondence analysis of microarray time-course data in case-control design Computational Bottom-Up Vulnerability Indicator for Low-Income Flood-Prone Urban Areas Correspondence Analysis for Groundwater Quality System Design and Implementation of Cryptographic Computation for Substation Security Communication Virtual structures and heterogeneous nodes in dependency graphs for detecting metamorphic malware Efficiency identity-based threshold group signature scheme Target Trajectory Querying in Wireless Sensor Networks Secured Data Sharing in Groups Using Attribute-Based Broadcast Encryption in Hybrid Cloud A Scalable Post-quantum Hash-Based Group Signature A Key Distribution and Management Scheme for Clustered Ad Hoc Sensor Networks ID-Based Aggregate Proxy Signature Scheme Realizing Warrant-Based Delegation A Survey on the Authorization Technology of Digital Signature Multi-Certification Signature Scheme and Its Application to Cross-Certification Experimenting with Anomaly Detection by Mining Large-Scale Information Networks Thermal camouflage pattern prediction using PRISM and PMO VOXEL-LEVEL MAPPING OF TRACER KINETICS IN PET STUDIES: A STATISTICAL APPROACH EMPHASIZING TISSUE LIFE TABLES. In-Depth Evaluation of Data Collected During a Continuous Pharmaceutical Manufacturing Process: A Multivariate Statistical Process Monitoring Approach. Assessment of spinach seedling health status and chlorophyll content by multivariate data analysis and multiple linear regression of leaf image features CHEMICAL NON-DESTRUCTIVE EVALUATION AT THE CENTER FOR PROCESS ANALYTICAL CHEMISTRY Bibliometrical Analysis on the Big Data Research in China Coarse-Graining Protein Structures With Local Multivariate Features from Molecular Dynamics Detection Models Study of Chlorophyll in Winter Wheat's Leaves by Reflectance Spectra and Artificial Neural Networks Infrared Spectral Characteristics of Electrical Injuries on Swine Skin Caused by Different Voltages Based on Machine Learning Algorithms. NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface THE METHOD OF MULTIVARIATE STATISTICAL ANALYSIS OF THE TIME MULTIVARIATE CRITICAL QUALITY ATTRIBUTES OF MANUFACTURE PROCESS WITH THE DATA FACTORIZATION МЕТОД БАГАТОВИМІРНОГО СТАТИСТИЧНОГО АНАЛІЗУ ЧАСОВИХ БАГАТОВИМІРНИХ КРИТИЧНИХ АТРИБУТІВ ЯКОСТІ ПРОЦЕСУ ВИРОБНИЦТВА З ФАКТОРИЗАЦІЄЮ ДАНИХ Remote sensing and machine learning for crop water stress determination in various crops: a critical review Modelling of Term Structure Dynamics Using Stochastic Processes Multivariate Equi-width Data Swapping for Private Data Publication Multivariate Nonnegative Quadratic Mappings Improved Minimum Entropy Filtering for Continuous Nonlinear Non-Gaussian Systems Using a Generalized Density Evolution Equation Low-complexity multidiscriminant FOPEN target screener Do randomized-response designs eliminate response biases? An empirical study of non-compliance behavior Real-Time Energy-Conserving VM-Provisioning Framework for Cloud-Data Centers
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