Statistical analysis with missing data
Statistical analysis with missing data
Empirical likelihood for single-index models
Journal of Multivariate Analysis
Empirical likelihood for semiparametric regression model with missing response data
Journal of Multivariate Analysis
Estimation and empirical likelihood for single-index models with missing data in the covariates
Computational Statistics & Data Analysis
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The purpose of this article is to use an empirical likelihood method to study the construction of confidence intervals and regions for the parameters of interest in linear regression models with missing response data. A class of empirical likelihood ratios for the parameters of interest are defined such that any of our class of ratios is asymptotically chi-squared. Our approach is to directly calibrate the empirical log-likelihood ratio, and does not need multiplication by an adjustment factor for the original ratio. Also, a class of estimators for the parameters of interest is constructed, and the asymptotic distributions of the proposed estimators are obtained. Our results can be used directly to construct confidence intervals and regions for the parameters of interest. 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 used for illustrating our methods.