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数学学院、所系列学术报告(764场):林华珍 教授

发表于: 2018-05-08 13:08  点击:

题目:Efficient Estimation and Computation for the Generalized Additive Models

with Unknown Link Function

报告人:林华珍 教授

时间:2018年5月9日10:00-11:00

地点:数学楼3楼会议室

报告人简介: 林华珍,西南财经大学统计学院教授、博导,统计研究中心主任,教育部长江学者特聘教授,国家杰出青年科学基金获得者,教育部新世纪优秀人才,第十一批四川省学术和技术带头人。 先后有论文发表在JASA、Annals of Statistics、JRSSB、Biometrika及Biometrcs等国际统计学顶级期刊上,并担任国际统计学权威期刊《Biometrics》、《Scandinavian Journal of Statistics》、《Statistics and Its Interface》Associate Editor。研究领域:非参数理论和方法、转换模型、生存数据分析、函数数据分析、时空数据分析。

摘要:The generalised additive models (GAM) are widely used in data analysis. In the application of the GAM, the link function involved is usually assumed to be a commonly used one without justification. Motivated by a real data example with binary response where the commonly used link function does not work, we propose a generalised additive models with unknown link function (GAMUL) for various types of data, including binary, continuous and ordinal. The proposed estimators are proved to be consistent and asymptotically normal. Semiparametric efficiency of the estimators is demonstrated in terms of their linear functionals. In addition, an iterative algorithm, where all estimators can be expressed explicitly as a linear function of Y, is proposed to overcome the computational hurdle for the GAM type model. Extensive simulation studies conducted in this paper show the proposed estimation procedure works very well. The proposed GAMUL are finally used to analyze a real data set about loan repayment in China, which leads to some interesting findings.