Testing Composite Null Hypotheses with High Dimensional Dependent Data

Abstract

Testing composite null hypotheses is fundamental to many scientific applications. Existing high dimensional composite null hypotheses testing often ignores the dependence structure among features, leading to overly conservative or liberal results. To address this limitation, we develop a four-state hidden Markov model (HMM) for bivariate $p$-value sequences arising from two-study replicability
analysis.  When applied to genome-wide association studies (GWAS), the proposed approach identifies novel biological findings that are missed by current methods.

Biography

Dr Cao Hong Yuan is a professor in the Department of Statistics, Florida State University.  Before joining FSU, she was an assistant professor in the department of Statistics, University of Missouri -Columbia, and an assistant professor in the Department of Public Health Sciences, University of Chicago. She has broad research interests including Causal Inference, Change point, high dimensional statistics inference, Multiple testing, survival analysis, etc.