标题：The Long March Towards Joint Asymptotics: My 1st Steps...

汇报人：Guang Cheng, Assistant Professor, Purdue University

功夫：2013-06-27(星期四) 14:00-15:00

地址：拉斯维加斯98882号楼217室

Abstract:

We consider the joint asymptotics and inferences for the semi-nonparametric models where an Euclidean parameter and an infinite dimensional parameter are both of interest. The joint inferences, e.g., joint hypothesis, are very useful in practice but its theoretical validity are very challenging to establish due to the different natures of two model components: parametric v.s. nonparametric. In this talk, we presents the first comprehensive studies on the joint local/global inferences in a unified asymptotic framework. The inference optimality/efficiency issues are also carefully addressed. For simplicity of illustration, we use the generalized partly linear models as the prototypical example. The methods of penalized estimation and local polynomial estimation are employed. The novel joint Bahadur representation is developed as the theoretical foundation of all our results. This talk can be viewed as the first step to exploring the intriguing joint asymptotics phenomena in statistics.