主讲人: Thuan Nguyen
主讲人简介：Associate Professor of Biostatistics in the Oregon Health & Science University-Portland State University (OHSU-PSU) School of Public Health. As a biostatistician with a research emphasis in statistical methodology, my research experience includes mixed effects models, statistical genetics, model selection, small area estimation, and longitudinal data analysis. Multiple publications in major journals in statistics and biostatistics, including the Annals of Statistics, Journal of the American Statistical Association, Biostatistics, and Statistics in Medicine.
摘要： Mixed effects models typically involve random effects, which, by definition, are unobserved. In some practical situations, however, some of these random effects are observed, or can be treated as observed. This occurs, for example, in the analysis of longitudinal data when some of the covariates are missing. One natural idea of handling the missing covariates is to treat them as unobserved random effects; then, the observed covariates correspond to random effects that are observed.
We propose a nonlinear mixed effects model, in which part of the random effects are observed. The model, called partially observed mixed effects model, may be viewed as a nonlinear mixed effects model, except that some of the random effects are observed. As a result, the method of inference is different from the standard procedure of mixed model analysis. We derive maximum likelihood (ML) estimation and establish asymptotic properties of the estimators. Furthermore, we
derive an empirical best predictor (EBP) of the missing covariates as well as a measure of uncertainty for the EBP. Finite-sample performance of the proposed estimators and predictors is evaluated via simulation studies. This work is joint with Xiaohui Liu of Jiangxi University of Finance & Economics, China and Jiming Jiang of the University of California, Davis, USA.