1. Valid inference after "double-dipping"
As data sets continue to grow in size, in many settings the focus of data collection has shifted away from testing pre-specified hypotheses, and towards hypothesis generation. Researchers are often interested in performing an exploratory data analysis in order to generate hypotheses, and then testing those hypotheses on the same data; I call this "double-dipping". Unfortunately, double dipping can lead to a highly inflated Type I error rate. Of late, I have been working on solving the "double-dipping" problem for a number of commonly-used analyses, using tools from the selective inference and post-selection inference literature.
Anna Neufeld, Lucy L. Gao, and Daniela Witten (2021+) Tree-values: selective inference for regression trees. [pdf] [website] [github] [code]
Lucy L. Gao, Jacob Bien, and Daniela Witten (2021+) Selective inference for hierarchical clustering. [pdf] [website] [github] [code]
2. Learning from Multi-View Data
In the multi-view data setting, multiple data sets (views) are available on a single common set of observations. For example, multivariate clinical and genomic data sets may be available on a single set of tissue samples, or we may have two network data sets that describe physical interactions and co-membership in protein complexes between a single set of proteins.
Lucy L. Gao, Daniela Witten and Jacob Bien (2021+) Testing for association in multi-view network data. To appear in Biometrics. [pdf] [cran] [code]
[Received a 2020 ASA Statistical Learning and Data Science Section Student Paper Award.]
Lucy L. Gao, Jacob Bien and Daniela Witten (2020) Are clusterings of multiple data views independent? Biostatistics, 21(4), 692-708. [pdf] [cran] [code]
[Received a 2019 ASA Biometrics Section Student Travel Award.]
3. Optimal Experiment Design
The number of replicates in experiments limits the amount of information that is available, but we maximize the amount of information gained by carefully choosing the values of the experimental inputs. This is the central problem of optimal experiment design.
Pengqi Liu, Lucy L. Gao and Julie Zhou (2021+). R-optimal designs for multi-response regression models with multi-factors. To appear in Communications in Statistics - Theory and Methods. [pdf]
Lucy L. Gao and Julie Zhou (2020). Minimax D-optimal designs for multivariate regression models with multi-factors. Journal of Statistical Planning and Inference . [pdf]
Lucy L. Gao and Julie Zhou (2017) D-optimal designs based on the second-order least squares estimator. Statistical Papers, 58(2): 77-94.
Lucy L. Gao and Julie Zhou (2014) New optimal design criteria for regression models with asymmetric errors. Journal of Statistical Planning and Inference, 149: 140-151.
During the first year of my Ph.D., I collaborated with researchers at the Seattle Children’s Research Institute to characterize liver transplantation offers to pediatric patients.
Evelyn Hsu, Michele Shaffer, Lucy L. Gao, Christopher Sonnenday, Michael Volk, John Bucuvalas and Jennifer Lai (2017) Analysis of liver offers to pediatric candidates on the transplant wait list. Gastroenterology, 153(4): 988-995.