Statistical analysis with missing data
Statistical analysis with missing data
On confidence bands in nonparametric density estimation and regression
Journal of Multivariate Analysis - Memorial volume dedicated to P. R. Krishnaiah
Estimation in partially linear models with missing responses at random
Journal of Multivariate Analysis
Generalized partially linear models with missing covariates
Journal of Multivariate Analysis
Empirical likelihood for linear models with missing responses
Journal of Multivariate Analysis
Estimation and empirical likelihood for single-index models with missing data in the covariates
Computational Statistics & Data Analysis
| Hi-index | 0.00 |
A bias-corrected technique for constructing the empirical likelihood ratio is used to study a semiparametric regression model with missing response data. We are interested in inference for the regression coefficients, the baseline function and the response mean. A class of empirical likelihood ratio functions for the parameters of interest is defined so that undersmoothing for estimating the baseline function is avoided. The existing data-driven algorithm is also valid for selecting an optimal bandwidth. Our approach is to directly calibrate the empirical log-likelihood ratio so that the resulting ratio is asymptotically chi-squared. Also, a class of estimators for the parameters of interest is constructed, their asymptotic distributions are obtained, and consistent estimators of asymptotic bias and variance are provided. Our results can be used to construct confidence intervals and bands for the parameters of interest. A simulation study is undertaken to compare the empirical likelihood with the normal approximation-based method in terms of coverage accuracies and average lengths of confidence intervals. An example for an AIDS clinical trial data set is used for illustrating our methods.