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Optimal Weighted Wilcoxon–Mann–Whitney Test for Prioritized Outcomes Read chapter Read first chapter

2018 | OriginalPaper | Chapter

3. Rank-Based Empirical Likelihood for Regression Models with Responses Missing at Random

Authors : Huybrechts F. Bindele, Yichuan Zhao

Published in: New Frontiers of Biostatistics and Bioinformatics

Publisher: Springer International Publishing

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Abstract

In this paper, a general regression model with responses missing at random is considered. From an imputed rank-based objective function, a rank-based estimator is derived and its asymptotic distribution is established under mild conditions. Inference based on the normal approximation approach results in under coverage or over coverage issues. In order to address these issues, we propose an empirical likelihood approach based on the rank-based objective function, from which its asymptotic distribution is established. Extensive Monte Carlo simulation experiments under different settings of error distributions with different response probabilities are considered. The simulation results show that the proposed approach has better performance for the regression parameters compared to the normal approximation approach and its least-squares counterpart. Finally, a data example is provided to illustrate our method.

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previous chapter A Selective Overview of Semiparametric Mixture of Regression Models
next chapter Bayesian Nonparametric Spatially Smoothed Density Estimation
Appendix
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Metadata
Title
Rank-Based Empirical Likelihood for Regression Models with Responses Missing at Random
Authors
Huybrechts F. Bindele
Yichuan Zhao
Copyright Year
2018
Book
New Frontiers of Biostatistics and Bioinformatics

Print ISBN: 978-3-319-99388-1

Electronic ISBN: 978-3-319-99389-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-99389-8_3

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