主 题：Exponentially tilted likelihood inference on growing dimensional unconditional moment models
内容简介：Growing-dimensional data with likelihood unavailable are often encountered in various fields. This paper presents a penalized exponentially tilted likelihood (PETL) for variable selection and parameter estimation for growing dimensional unconditional moment models in the presence of correlation among variables and model misspecification. Under some regularity conditions, we investigate the consistent and oracle properties of the PETL estimators of parameters, and show that the constrainedly PETL ratio statistic for testing contrast hypothesis asymptotically follows the central chi-squared distribution. Theoretical results reveal that the PETL approach is robust to model misspecification. We also study high-order asymptotic properties of the proposed PETL estimators. Simulation studies are conducted to investigate the finite performance of the proposed methodologies. An example from the Boston Housing Study is illustrated.
报告人：唐年胜 教授 博导 院长
特聘教授
“国家杰出青年科学基金”获得者
教育部“新世纪优秀人才支持计划”入选者
云南省“中青年学术和技术带头人”
时 间： 2016-06-03 15:00
地 点：竞慧东楼302
举办单位：理学院 科研部
