“Tests for Covariance Structures with High-dimensional Repeated Measurements”
Dr. Ping-Shou Zhong, Michigan State University
Date: Thursday, September 17, 2015
Time: 3:30 PM – 4:30 PM
Location: 269 Everitt
Sponsor: Department of Statistics
In regression analysis with repeated measurements, such as longitudinal data and panel data, structured covariance matrices characterized by a small number of parameters have been widely used and play an important role in parameter estimation and statistical inference. To assess the adequacy of a specified covariance structure, one often adopts the classical likelihood-ratio test when the dimension of the repeated measurements (p) is smaller than the sample size (n). However, the assessment becomes challenging when p is bigger than n, since the classical likelihood-ratio test is no longer applicable. This talk will focus an adjusted goodness-of-fit test, which is designed to examine a broad range of covariance structures under the scenario of “large p, small n”. The analytical examples will be presented to illustrate the effectiveness of adjustment for assessing the goodness-of-fit of covariance. In addition, large sample properties of the proposed test are established. Moreover, simulation studies and a real data example are provided to demonstrate the finite sample performance and the practical utility of the test.