Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three...

Full description

Saved in:
Bibliographic Details
Main Authors: Hanji He, Guangming Deng
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2020/8893594
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832547682770485248
author Hanji He
Guangming Deng
author_facet Hanji He
Guangming Deng
author_sort Hanji He
collection DOAJ
description We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.
format Article
id doaj-art-0784c68d5e7f407d9a10d98ba0acb629
institution Kabale University
issn 1026-0226
1607-887X
language English
publishDate 2020-01-01
publisher Wiley
record_format Article
series Discrete Dynamics in Nature and Society
spelling doaj-art-0784c68d5e7f407d9a10d98ba0acb6292025-02-03T06:43:43ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/88935948893594Mean Empirical Likelihood Inference for Response Mean with Data Missing at RandomHanji He0Guangming Deng1College of Science, Guilin University of Techology, Guilin 541004, ChinaCollege of Science, Guilin University of Techology, Guilin 541004, ChinaWe extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.http://dx.doi.org/10.1155/2020/8893594
spellingShingle Hanji He
Guangming Deng
Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random
Discrete Dynamics in Nature and Society
title Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random
title_full Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random
title_fullStr Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random
title_full_unstemmed Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random
title_short Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random
title_sort mean empirical likelihood inference for response mean with data missing at random
url http://dx.doi.org/10.1155/2020/8893594
work_keys_str_mv AT hanjihe meanempiricallikelihoodinferenceforresponsemeanwithdatamissingatrandom
AT guangmingdeng meanempiricallikelihoodinferenceforresponsemeanwithdatamissingatrandom