9月18日讲座:Approximate Bayesian Inference for Penalized Spline Models: Methods, Applications and Case Studies——王晓峰博士

2012-09-15 中心编辑

厦门大学统计学高级系列讲座2012秋季学期第一讲(总第13讲)——王晓峰博士

题目:” Approximate Bayesian Inference for Penalized Spline Models: Methods, Applications and Case Studies ”

讲座人:王晓峰博士(美国克利夫兰医学中心勒纳研究所)

时间: 2012年9月18日(星期二)下午4:15-5:45

地点:经济楼D110

主办单位:王亚南经济研究院     经济学院

摘要:Penalized splines are often attractive in handling the nonlinear effects in additive regression models because of their flexibility. In this talk, we first review classical nonparametric smoothing approaches in statistics, and discuss frequentist and Bayesian analysis of penalized spline regression. A new statistical technique, known as integrated nested Laplace approximation (INLA), is then applied to implement approximate Bayesian inference for a large class of penalized spline models. It is shown that INLA provides not only accurate but also computationally fast approximations to the posterior marginals of regression parameters. A variety of applications and case studies, including functional data analysis, generalized semiparametric mixed models, and spatial econometrics, will be discussed by applying the penalized splines with INLA.