Prof. Bin Nan 南斌 教授
University of California, Irvine
This research develops nonparametric time‑varying coefficient models for longitudinal data subject to terminal events and right censoring, with kernel smoothing for estimation. It falls within Professor Bin Nan’s core research areas: longitudinal and survival analysis, semiparametric and nonparametric inference, and statistical machine learning.
Abstract 摘要
We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the follow up time and the residual lifetime. The proposed model extends our earlier work on parametric conditional approach given terminal event time, thus avoids potential model misspecification. We consider a kernel smoothing method for estimating regression coefficients in our model and use cross-validation for bandwidth selection, applying undersmoothing in the final analysis to eliminate the asymptotic bias of the kernel estimator. We show that the kernel estimates follow a finite-dimensional normal distribution asymptotically under mild regularity conditions and provide an easily computable sandwich covariance matrix estimator. We conduct extensive simulations that show desirable performance of the proposed approach and apply the method to analyzing the medical cost data for patients with end-stage renal disease.
Speaker Biography 讲者简介
Bin Nan is Chancellor's Professor of Statistics at the University of California, Irvine. He obtained his Ph.D. degree in 2001 from the Department of Biostatistics at the University of Washington. He had been on faculty in the Department of Biostatistics at the University of Michigan from 2001 to 2017, meanwhile holding a courtesy appointment in the Department of Statistics at the University of Michigan. He joined UC Irvine in 2017. Professor Nan's research interests are in various areas of statistics and biostatistics. He studies semiparametric inference, failure time and survival analysis, longitudinal data analysis, missing data and two-phase sampling designs, high-dimensional data analysis and machine learning methodology. He also collaborates on projects in the areas of epidemiology, bioinformatics, and brain imaging, particularly in Alzheimer's disease research. His research activities are constantly supported by NSF and NIH grants. He is Fellow of the Institute of Mathematical Statistics, Fellow of the American Statistical Association, and Elected Member of the International Statistical Institute.
Topics 涉及主题
Longitudinal and survival data analysis Semiparametric & nonparametric statistical inference High-dimensional data and statistical machine learning