报告时间：2019年7月11日 15:40-16:40
报告地点：明德主楼1016会议室
报告题目:Estimation and Inference of A Heteroskedasticity Model with Latent Semiparametric Factors for Panel Data Analysis
报告摘要:
We consider estimation and inference of a flexible subject-specific heteroskedasticity model for analyzing large scale panel data, which employs latent semiparametric factor structure to simultaneously account for the heteroskedasticity across subjects and contemporaneous correlations. Specifically, the heteroskedasticity across subjects is modeled by the product of unobserved stationary process of factors and subject-specific covariate effect. Serving as the loading, the covariate effect is further modeled through the additive model. We propose a two-step procedure for estimation. First, the latent factor process and nonparametric loading are estimated via projection-based methods. The estimation of regression coefficients is further conducted through the generalized least squares type approach. Theoretical validity of the two-step procedure is carefully documented. By scrupulously examining the non-asymptotic rates for recovering the latent factor process and its loading, we further study the properties of the estimated regression coefficients. In particular, we establish the asymptotic normality of the proposed two-step estimate of regression coefficients. The proposed regression coefficient estimator is also shown to be asymptotically efficient. This leads to a more efficient confidence set of the regression coefficients. Using a comprehensive simulation study, we demonstrate the finite sample performance of the proposed procedure, and numerical results corroborate our theoretical findings. Finally, we apply our proposed method to a data set of air quality and energy consumption collected at 129 monitoring sites in the United States in 2015.
报告人简介：
Wen Zhou is an Assistant Professor in the Department of Statistics at Colorado State University. He obtained his Ph.D. degrees in Applied Mathematics and Statistics at Iowa State University in 2010 and 2014. Dr. Zhou’ s research mainly focuses on developing computational methods, statistical models and inference procedures to study data of high-dimensionalities from genomic and biomedical studies. Dr. Zhou has experience on building theoretically justified statistical models and procedures for analyzing different types of omics data to draw biologically critical insights. He has developed inference procedures for different statistical problems for high-dimensional data, including testing for the structures of high-dimensional covariance matrix, comparing large covariance matrices with complex unknown structures and a novel gene clustering algorithm; testing high-dimensional mean vectors with unknown complex dependency; identification of pairwise informative features for clustering data with growing dimensions; and detection of spurious discoveries in genomic studies using a nonparametric procedure.