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Jackknife empirical likelihood of error variance for partially linear varying-coefficient model with missing covariates

Yuye Zou, Chengxin Wu, Guoliang Fan and Riquan Zhang

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 6, 1744-1766

Abstract: In this paper, we apply the profile least-square method and inverse probability weighted method to define estimation of the error variance in partially linear varying-coefficient model when the covariates are missing at random. At the same time, we construct a jackknife estimator and jackknife empirical likelihood (JEL) statistic of the error variance, respectively. It is proved that the proposed estimators are asymptotical normality and the JEL statistic admits a limiting standard chi-square distribution. A simulation study is conducted to compare the JEL method with the normal approximation approach in terms of coverage probabilities and average interval lengths, and a comparison of the proposed estimators is done based on sample means, biases and mean square errors under different settings. Subsequently, a real data set is analyzed for illustration of the proposed methods.

Date: 2023
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DOI: 10.1080/03610926.2021.1938128

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