题目：Efficient Inference for Nonlinear State Space Models: an Automatic Sample Size Selection Rule 

摘要：This talk studies the maximum likelihood estimation of nonlinear state space models. Particle Markov chain Monte Carlo method is introduced to implement the Monte Carlo expectation maximization algorithm for more accurate and robust estimation. Under this framework, an automated sample size selection criterion is constructed via renewal theory. This criterion would increase the sample size when the relative likelihood indicates that the parameters are close to each other. The proposed methodology is applied to the stochastic volatility model and another nonlinear state space model for illustration, where the results show better estimation performance. 

主讲人：程璟博士  

报告时间：2019年12月5日10:30-12:00  

报告地点：精工楼7-104 

报告人简介：

　　程璟，华侨大学统计学院讲师，香港中文大学统计学博士，厦门大学经济学学士、经济学硕士，曾在Computational Statistics & Data Analysis以及Journal of Theoretical Biology等杂志发表论文，以第二作者出版《数理统计》教材一部。