讲座题目：Bayesian Robust Estimation of Partially Functional Linear Regression Models Using Heavy-tailed Distributions

讲座时间：2019年5月24日（周五）13:30-14:30

讲座地点：6号学院楼402会议室

主办单位：伟德BETVlCTOR1946源于英国

摘要：Functional linear regression is a popular method to model the relationship between a scalar response and functional predictors. A common estimation procedure is using maximum likelihood by assuming normal distributions for residual errors; however this method may make inferences vulnerable to the presence of outliers. In this article, we introduce a robust structure of partially functional linear model by considering a class of scale mixtures of normal distributions for residual errors. A Bayesian framework is adopted, and an MCMC algorithm is developed to carry out posterior inference on model parameters. The finite sample performance of our proposed method is evaluated by using some simulation studies and a real dataset.

主讲人简介：

刘柏森博士，毕业于加拿大McGill大学数学与统计系，现任职于东北财经大学统计学院，长期从事非参数统计和高维数据统计推断的研究。近年来在Computational Statistics & Data Analysis、Journal of Multivariate Analysis、Journal of Statistical Planning and Inference等SCI期刊上已公开发表多篇学术论文，其研究领域主要包括：函数型数据分析、高维数据统计推断和微分方程模型的统计推断等。

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