A0292
Title: Sparse positive-definite estimation for covariance matrices with repeated measurements
Authors: Yuedong Wang - University of California - Santa Barbara (United States) [presenting]
Abstract: Repeated measurements arise in many areas, such as epidemiology, medicine, psychology, and neuroscience, where random variables are measured multiple times across different subjects. In such settings, dependence structures among random variables that are between subjects and within a subject may be different. Ignoring this fact may lead to misleading and questionable analytic results. The problem of simultaneous sparse and positive-definite estimation is studied for the between-subject and within-subject covariance matrices. The convergence rates are established for the proposed between-subject and within-subject covariance matrix estimators under some regularity conditions. In general, the convergence rate for the within-subject covariance matrix estimator depends on the total number of observations, while the convergence rate for our between-subject covariance matrix estimator is affected by the number of groups and is insensitive to the imbalance of the data. The finite-sample performance of the proposed estimators is illustrated numerically in comprehensive simulations and a real data application.
