Jackknife empirical likelihood for the error variance in linear errors-in-variables models with missing data

Measurement errors and missing data are often arise in practice. Under this circumstance, we focus on using jackknife empirical likelihood (JEL) and adjust jackknife empirical likelihood (AJEL) methods to construct confidence intervals for the error variance in linear models. Based on residuals of t...

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 51; no. 14; pp. 4827 - 4840
Main Authors Xu, Hong-Xia, Fan, Guo-Liang, Wang, Jiang-Feng
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 18.07.2022
Taylor & Francis Ltd
Subjects
Confidence interval
Confidence intervals
Error correction
error variance
errors-in-variables
Jackknife empirical likelihood
Likelihood ratio
Missing data
Statistical analysis
Variance
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Summary:Measurement errors and missing data are often arise in practice. Under this circumstance, we focus on using jackknife empirical likelihood (JEL) and adjust jackknife empirical likelihood (AJEL) methods to construct confidence intervals for the error variance in linear models. Based on residuals of the models, the biased-corrected inverse probability weighted estimator of the error variance is introduced. Furthermore, we propose the jackknife estimator, jackknife and adjust jackknife empirical log-likelihood ratios of the error variance and establish their asymptotic distributions. Simulation studies in terms of coverage probability and average length of confidence intervals are conducted to evaluate the proposed method. A real data set is used to illustrate the proposed JEL and AJEL methods.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2020.1824274