Weighted empirical likelihood inference for multiple samples |
| |
Authors: | Yuejiao Fu Xiaogang Wang Changbao Wu |
| |
Institution: | 1. Department of Mathematics and Statistics, York University, Toronto, ON, Canada M3J 1P3;2. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1 |
| |
Abstract: | We propose a weighted empirical likelihood approach to inference with multiple samples, including stratified sampling, the estimation of a common mean using several independent and non-homogeneous samples and inference on a particular population using other related samples. The weighting scheme and the basic result are motivated and established under stratified sampling. We show that the proposed method can ideally be applied to the common mean problem and problems with related samples. The proposed weighted approach not only provides a unified framework for inference with multiple samples, including two-sample problems, but also facilitates asymptotic derivations and computational methods. A bootstrap procedure is also proposed in conjunction with the weighted approach to provide better coverage probabilities for the weighted empirical likelihood ratio confidence intervals. Simulation studies show that the weighted empirical likelihood confidence intervals perform better than existing ones. |
| |
Keywords: | Common mean models Heteroscedasticity Multiple samples Related samples Stratified sampling Weighted likelihood |
本文献已被 ScienceDirect 等数据库收录! |
|