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

內(nèi)容簡(jiǎn)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(bào)告人：唐年勝    教授    博導(dǎo)    院長(zhǎng)

                    特聘教授

                   “國(guó)家杰出青年科學(xué)基金”獲得者

                    教育部“新世紀(jì)優(yōu)秀人才支持計(jì)劃”入選者

                    云南省“中青年學(xué)術(shù)和技術(shù)帶頭人”

時(shí)  間：    2016-06-03    15:00

地  點(diǎn)：競(jìng)慧東樓302

舉辦單位：理學(xué)院  科研部