主  題：Exponentially tilted likelihood inference on growing dimensional unconditional moment models

內容簡介：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.

報告人：唐年勝    教授    博導    院長

                    特聘教授

                   “國家杰出青年科學基金”獲得者

                    教育部“新世紀優秀人才支持計劃”入選者

                    云南省“中青年學術和技術帶頭人”

時  間：    2016-06-03    15:00

地  點：競慧東樓302

舉辦單位：理學院  科研部