主讲人:美国加州大学圣巴巴拉分校统计与应用概率系 Yuedong Wang教授
主题:Semi-parametric Nonlinear Mixed Effects Models and Their Applications
主持人:林华珍教授
时 间:2013年7月11日下午14:00-15:00
地 点:通博楼B212会议室
主办:统计学院 科研处
摘 要:
NonLinear Mixed effects Models (NLMM) and SElf-MOdeling nonlinear Regression (SEMOR) models are often used to fit repeated measures data. They use a common function shared by all subjects to model variation within each subject and some fixed and/or random parameters to model variation between subjects. The parametric NLMM may be too restrictive and the semi-parametric SEMOR model ignores correlations within each subject. We propose a class of Semi-parametric Nonlinear Mixed effects Models (SNMM) which extend NLMMs, SEMOR models and many other existing models in a natural way. A SNMM assumes that the mean function depends on some parameters and nonparametric functions. The parameters provide an interpretable data summary. The nonparametric functions provide flexibility to allow the data to decide some unknown/uncertain components, such as the shape of the mean response over time. A second stage model with fixed and random effects is used to model the parameters. Smoothing splines are used to model the nonparametric functions. Covariate effects on parameters can be built into the second stage model, and covariate effects on nonparametric functions can be constructed using smoothing spline ANOVA decompositions. We fit SNMMs to investigate cortisol circadian rhythms in normal subjects, patients with major depression and patients with Cushing's syndrome.
主讲人介绍:
Yuedong Wang, 美国加州大学圣巴巴拉分校统计与应用概率系教授,主要研究领域包括生物统计建模,bootstrap, 广义线性模型,纵向数据分析,基因序列数据分析,混合效用模型,模型选择,生存分析等,在包括JASA等顶尖杂志发表论文60余篇。主讲人详细介绍请参看http://www.pstat.ucsb.edu/faculty/yuedong/CV.pdf