缅北强奸

Large scale multivariate regression with many heavy-tailed responses arises in a wide range of areas from genomics, financial asset pricing, banking regulation, to psychology and social studies. Simultaneously testing a large number of general linear hypotheses, such as multiple contrasts, based on the large scale multivariate regression reveals a variety of associations between responses and regression or experimental factors. Traditional multiple testing methods often ignore the effect of heavy-tailedness in the data and impose joint normality assumption that is arguably stringent in applications. This results in unreliable conclusions due to the lose of control on the false discovery proportion/rate (FDP/FDR) and severe compromise of power in practice. In this paper, we employ data-adaptive Huber regression to propose a framework of joint robust inference of the general linear hypotheses for large scale multivariate regression. With mild conditions, we show that the proposed method produces consistent estimate of the FDP and FDR at a prespecified level. Particularly, we employ a bias-correction robust covariance estimator and study its exponential-type deviation inequality to provide theoretical guarantee of our proposed multiple testing framework. Extensive numerical experiments demonstrate the gain in power of the proposed method compared to OLS and other procedures.

Wen Zhou is an Assistant Professor in the Department of Statistics at the Colorado State Unversity. His research focuses on high dimensional data inference, graphical modeling, statistical machine learning, statistical genomics and bioinformatics, system biology, optimization and game theory.