理学院学术报告20190703(一)
发布人:胡彬  发布时间:2019-07-01   浏览次数:92

On High-dimensional Misspecified Mixed Model Analysis and

Genome-wide Association Study

    报告人: Jiming JiangUniversity of California, Davis, USA

    时   间:201973日下午 14:30

    地   点:核工楼1216

    摘   要:We study behavior of the restricted maximum likelihood (REML) estimator under a misspecified linear mixed model (LMM) that has received much attention in recent genome-wide association studies. The asymptotic analysis establishes consistency of the REML estimator of the variance of the errors in the LMM, and convergence in probability of the REML estimator of the variance of the random effects in the LMM to a certain limit, which is equal to the true variance of the random effects multiplied by the limiting proportion of the nonzero random effects present in the LMM. The asymptotic results also establish convergence rate (in probability) of the REML estimators as well as a result regarding convergence of the asymptotic conditional variance of the REML estimator. The asymptotic results are fully supported by the results of empirical studies, which include extensive simulation studies that compare the performance of the REML estimator (under the misspecified LMM) with other existing methods, and real data applications (only one example is presented) that have important genetic implications.

    报告人简介Dr. Jiming Jiang received his B.S. degree in Mathematics in 1985 and his M.S. degree in Probability and Statistics in 1988, both from Peking University, China. He received his Ph.D. degree in Statistics in 1995 from the University of California, Berkeley, USA. He was an Assistant Professor at Case Western Reserve University, USA and Associate Professor at the University of California, Davis, USA. He is currently a Tenured Professor at the University of California, Davis, USA.

His research interests include mixed effects models, model selection, small area estimation, longitudinal data analysis, Big Data intelligence, statistical genetics/bioinformatics, pharmacokinetics, and asymptotic theory.

  He is author of five books and monographs, including Linear and Generalized Linear Mixed Models and Their Applications(Springer 2007), Large Sample Techniques for Statistics(Springer 2010), The Fence Methods(World Scientific 2015; joint with Nguyen), Asymptotic Analysis of Mixed Effects Models: Theory, Application, and Open Problems (Chapman & Hall/CRC, 2017), and Robust Mixed Model Analysis (World Scientific 2019).

He has served editorial boards of several major statistical journals including TheAnnals of Statistics and Journal of the American Statistical Association, arguably the top two journals of statistical science. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. He is a co-recipient of the Chinese National Natural Science Award (Third-Prize, 1996) and Outstanding Statistical Application Award (American Statistical Association, 1998), the first co-recipient of Distinguished Alumni Award (National Institute of Statistical Sciences, 2015), and a Yangtze River Scholar (Chaired Professor, 2017).