Efficient Estimation and Computation for the Generalized Additive Models with Unknown Link Function
报 告 人:: 林华珍
报告地点:: 数学与统计学院四楼报告厅
报告时间:: 2018年01月11日星期四14:00-15:00

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.

发 布 人:科研助理 发布时间: 2018-01-09
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,美国华盛顿大学生物统计系博士后,四川大学博士。教育部长江学者特聘教授,国家杰出青年科学基金获得者,入选国家百千万人才工程,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。 论文发表在JASA、Annals of Statistics、JRSSB、Biometrika、Journal of Econometrics及Biometrcs等国际统计学及计量经济学顶级期刊上,并先后担任国际统计学期刊《Biometrics》、《Scandinavian Journal of Statistics》、《Statistics and Its Interface》、《Statistical Theory and Related Fields》Associate Editor, 国内核心学术期刊《应用概率统计》、《系统科学与数学》、《数理统计与管理》编委。 研究领域:非参数理论和方法、转换模型、生存数据分析、函数型数据分析、时空数据分析。