Friday October 2nd, at 11:00AM in Fretwell 116

Abstract: Longitudinal studies have proven to be useful in studying changes of response over time, and have been widely conducted in practice. It is common that longitudinal studies collect a large number of covariates, some of which are unimportant in explaining the response. Including such covariates in modelling and inferential procedures would greatly degrade the quality of the results. Moreover, longitudinal data analysis is challenged by the presence of measurement error and missing observations. In this talk, I will discuss the issues induced from these features, and describe simultaneous variable selection and estimation procedures that handle high dimensional longitudinal data with missingness and measurement error.