报 告 人：黄振生 教授
南京理工大学
报告题目：Adaptive profile-empirical-likelihood inferences for generalized single-index models
报告时间：2014年11月7日（周五）下午3:00
报告地点：静远楼1506学术报告厅
报告摘要：We study generalized single-index models and propose an efficient equation for estimating the index parameter and unknown link function, deriving a quasi-likelihood-based maximum empirical likelihood estimator (QLMELE) of the index parameter. We then establish an efficient confidence region for any components of the index parameter using an adaptive empirical likelihood method. A pointwise confidence interval for the unknown link function is also established using the QLMELE. Compared with the normal approximation proposed by Cui et al. [Ann Stat. 39 (2011) 1658], our approach is more attractive not only theoretically but also empirically. Simulation studies demonstrate that the proposed method provides smaller confidence intervals than those based on the normal approximation method subject to the same coverage probabilities. Hence, the proposed empirical likelihood is preferable to the normal approximation method because of the complicated covariance estimation. An application to a real data set is also illustrated.
黄振生教授简介：
南京理工大学统计与金融数学系，教授，博士，华东师范大学获博士学位，获上海市优博论文奖，澳大利亚联邦政府"奋进"奖，多次赴新加坡南洋理工大学，澳大利亚CSIRO以及香港等地合作研究，主持国家自然基金2项，发表学术论文40余篇。