Xiaoyu Li: Candidate for position in Statistics

Xiaoyu Li
Candidate for position in Statistics 

Title: Lack-of-fit testing of a regression model with response missing at random.

Abstract: Missing-data problem is a widely discussed topic in many areas while the minimum distance method is a classical method for model checking problems. This paper analyzes the linear regression model with response missing at random by imputation and minimum distance method. We propose a class of lack-of-fit tests for fitting a linear regression model when response variables are missing at random. These tests are based on a class of minimum integrated square distances between a kernel type estimator of a regression function and the parametric regression function being fitted. These tests are shown to be consistent against a large class of fixed alternatives. The corresponding test statistics are shown to have asymptotic normal distributions under null hypothesis and a class of  nonparametric local alternatives. Some simulation results are also presented.