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A High-Dimensional Nonparametric Multivariate Test for Mean Vector

作者: 发布时间:2014-12-03 点击数:
主讲人:Runze Li
主讲人简介:

 Runze Li, Distinguished Professor of Statistics, The Pennsylvania State University

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讲座简介:

 Abstract:

This work is concerned with testing the population mean vector of nonnormal high-dimensional multivariate data. Several tests for high-dimensional mean vector, based on modifying the classical Hotelling T2 test, have been proposed in the literature. Despite their usefulness, they tend to have unsatisfactory power performance for heavy-tailed multivariate data, which frequently arise in genomics and quantitative finance. This paper proposes a novel high-dimensional nonparametric test for the population mean vector for a general class of multivariate distributions. With the aid of new tools in modern probability theory, we proved that the limiting null distribution of the proposed test is normal under mild conditions when the dimension p is substantially larger than the sample size n. We further study the local power of the proposed test and compare its relative efficiency with a modified Hotelling T2 test for high-dimensional data. An interesting finding is that the newly proposed test can have even more substantial power gain with large p than the traditional nonparametric multivariate test does with finite fixed p. We study the finite sample performance of the proposed test via Monte Carlo simulations. We further illustrate its application by an empirical analysis of a genomics data set.
时间:2014-12-03(星期三)16:30-18:00
地点:Economics Building N302
讲座语言:English
主办单位:WISE & SOE
承办单位:Department of Statistics
期数:威尼斯37266高级计量经济学与统计学系列讲座2014秋季学期第八讲(总第51讲)
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