2019年杏盛學術前沿講座（23）
主 講 人🤚🏽：王所進 教授 德克薩斯A&M大學
主題名稱😖：Oracally Efficient Estimation and Simultaneous Inference in Partially Linear Single-index Models for Longitudinal Data
簡要內容：
In this presentation, we discuss oracally efficient estimation and asymptotically accurate simultaneous confidence band (SCB) for the nonparametric link function in the partially linear single-index models for longitudinal data. The proposed procedure works for possibly unbalanced longitudinal data under general conditions. The link function estimator is shown to be oracally efficient in the sense that it is asymptotically equivalent in the order of one over root n to that with all true values of the parameters being known oracally. Furthermore, the asymptotic distribution of the maximal deviation between the estimator and the true link function is provided, and hence an SCB for the link function is constructed. Finite sample simulation studies are carried out which support our asymptotic theory. The proposed SCB is applied to analyze a CD4 data set.
時間地點🤶：2019年6月20日下午3：00-4：30 地點：經濟管理學院335 室
主辦學院：經濟管理學院
杏盛娱乐
2019.5.30