商务统计与经济计量系学术报告（08年第十一，十二期）

题 目：Analysis of Longitudinal Data in the Presence of Informative Observational Times and a

Dependent Terminal Event, with Application to Medical Cost Data

报告人：Lei Liu（assistant professor in Biostatistics at the University of Virginia）

时间：2008年11月12日（周三）9：30-10：30

地 点：成人直播新楼214

摘 要：

In longitudinal observational studies, repeated measures are often taken at informative observation times. Also, there may exist a dependent terminal event such as death that stops the follow up. For

example, patients in poorer health are more likely to seek medical treatment and their medical cost for each visit tends to be higher. They are also subject to a higher mortality rate. In this paper we

propose a random effects model of repeated measures in the presence of both informative observation times and a dependent terminal event. Three submodels are used, respectively, for (1) the intensity of recurrent observation times, (2) the amount of repeated measures at each observation time, and (3) the hazard of death. Correlated random effects are incorporated to join the three submodels. The estimation can be conveniently accomplished by Gaussian quadrature

techniques, e.g., SAS Proc NLMIXED. An analysis of the cost accrual process of chronic heart failure patients from the clinical data repository (CDR) at the University of Virginia Health System is presented to illustrate the proposed method.

题 目：Interplay of Sparse Representation and Model Selection

报告人：XiaomingHuo(霍晓明)

Georgia Institute of Technology Atlanta GA USA

时 间：2008年11月12日（周三）上午10：50-11：50

地 点：成人直播新楼214

摘要：

Sparse representation is an active research field and has benefited tremendously from L‐1 based methodology. Recently, new modification (that utilizes reweighting, etc ) was proposed in both statistics and compressive sensing communities. We report theoretical and experimental results in this line of research. I will discuss how some results on single vectors can be generalized to cases with multiple vectors‐‐i.e., multivariate regression. We hope to demonstrate that there is a strong similarity between the theory of sparse representation and model selection: new results in one field can be handily transfer into another.