题目：Estimation and Empirical Likelihood for Single-index Models with Missing Data in the Covariates
报告人：薛留根 教授(北京工业大学应用数理学院）
时间：2016年11月11日（周五）上午10:00开始
地点：探花视频
致远楼102室
【摘要】The paper studies the estimation and empirical likelihood for single-index models with missing covariates. A generalized estimating equations estimator for index parameter with missing covariates is constructed, and its asymptotic distribution is obtained. The local linear estimator for link function achieves optimal convergence rate. By using the bias-correction and inverse selection probability weighted methods, a class of empirical likelihood ratios is proposed such that each of our class of ratios is asymptotically chi-squared. A simulation study indicates that the proposed methods are comparable in terms of coverage probabilities and average lengths/areas of confidence intervals/regions. An example of a real data set is illustrated.
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