a mixture model of outliers and clean data. A similar idea to k-nearest-neighbor approach is used in [@article:DMKD_chawla_10] to classify outliers as the data points that are less similar to a number of neighboring measurements. Surveys of advances in clustering the data into outliers and clean data can be found in [@article:AIR_hodge_04; @article:ITSP_yin_13; @conf:ICASSP_fritsche_09]. While these approaches might result in high estimation accuracy, they typically require large datasets [@article:ISPM_zoubir_12].

M-estimators [@book:RS_huber], in the contrary to identification-based methods, do not require pre-processing and can be used in non-Gaussian noise conditions. In principle, M-estimators can be seen as generalization of MLE and rely on solving a minimization problem of some loss function. For a detailed discussion on different loss functions, see [@book:RS_huber]. Since minimization problems are typically solved numerically based on the derivative of the loss function [@book:RSTM_maronna], they might converge to local minima.

In this work, we strive to find minimum variance unbiased (MVU) estimators for the location of estimation problems for non-Gaussian noise distributions where multiple distributions with positive support are considered. In case where MVU is not found, we introduce unbiased order-statistic-based estimators and compare their variances against the BLUE. The MVU estimators without any knowledge of the hyper 