American Statistical Association
New York City
Metropolitan Area Chapter

Levin Lecture Series: Spring 2018 Colloquium Seminars
Department of Biostatistics
Columbia University



STATISTICAL INFERENCE FOR HIGH-DIMENSIONAL LONGITUDINAL DATA

by

Runze Li
Verne M. Willaman Professor, Department of Statistics
Penn State University

Host: Dr. Min Qian


Abstract

This paper is concerned with statistical inference for longitudinal data with ultrahigh dimensional covariates. We first study the problem of constructing confidence intervals and hypothesis tests for a low dimensional parameter of interest. The major challenge is how to construct an optimal test statistic in the presence of high dimensional nuisance parameters and the sophisticated dependence among measurements. To deal with the challenge, we propose a quadratic decorrelated inference function approach, which simultaneously removes the impact of nuisance parameters and incorporates the correlation to enhance the efficiency of the estimation procedure. We prove that the proposed estimator is asymptotically normal and attains the semiparametric information bound, based on which we can construct an optimal test statistic for the parameter of interest. We conduct simulation studies to assess the finite sample performance of the proposed procedures. Our simulation results implies that the newly proposed procedure can control both Type I error for testing a low dimensional parameter of interest. We illustrate the proposed procedure by a real data example.

This is joint work with Ethan Fang and Yang Ning.


Date: Thursday, April 26, 2018
Time: 11:30 A.M. - 12:30 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
AR Building
8th Floor Auditorium
New York, New York

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