报告人:   王海斌  教授

报告时间：2017年4月7日（周五）17:00—18:00
报告地点：威廉希尔学术报告厅

报告摘要: We propose a latent semi-parametric model for ordinal data in which the single-index model is used to evaluate the effects of the latent covariates on the latent response. We develop a Bayesian sampling-based method with free-knot splines to analyze the proposed model. As the index may vary from minus infinity to plus infinity, the traditional spline that is defined on a finite interval can not be applied directly to approximate the unknown link function. We consider a modified version to address this problem by first transforming the index into the unit interval via a continuously cumulative distribution function and then constructing the spline bases on the unit interval. To obtain a rapidly-convergent algorithm, we make use of the partial-collapse and parameter expansion and reparameterization techniques, improve the movement step of Bayesian splines with free-knots so that all the knots can be relocated each time instead of only one knot, and design a generalized Gibbs step. We check the performance of the proposed model and estimation method by a simulation study, and apply them to analyze a real dataset.

报告人简介: 王海斌，厦门大学数学科学学院教授、博士生导师。1989年本科毕业于信阳师范学院数学系数学教育专业，1992年和2003年在东南大学分别获得应用数学专业硕士学位和系统工程专业博士学位。主要从事潜在变量模型、非/半参数模型及时间序列分析的研究工作，多次应邀赴香港中文大学统计系进行合作研究。目前已发表学术论文30余篇，主持1项国家自然科学基金（面上项目），参与1项国家自然科学基金（地区项目）。

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                                                    威廉希尔

                                  2017年3月22日