Hypothesis test on response mean with inequality constraints under data missing when covariables are present

In this article, a weighted empirical likelihood technique for constructing the empirical likelihood confidence regions is applied to study the heteroscedastic varying coefficient partially non-linear models with missing response data. We first provide an estimator of the error variance based on the Nadaraya-Watson kernel estimation method. Then a weighted empirical log-likelihood ratio of the unknown parameter is constructed based on the inverse probability weighted technique. The maximum empirical likelihood (MEL) estimator of the unknown parameter is obtained. Further, a weighted empirical log-likelihood ratio of the varying coefficient function is introduced based on the MEL estimator and the inverse probability weighted method. The limiting distributions of the resulting statistics for both the unknown parameter and varying coefficient function are shown to have the standard chi-squared distribution. A simulation study and a real data set example are undertaken to investigate the finite sample performance of the proposed methods.

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Weighted empirical likelihood for heteroscedastic varying coefficient partially non‐linear models with missing data

Fan

Wang

2021

Stat

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CLT for integrated square error of density estimators with censoring indicators missing at random

Zou

Liang

2019

Stat Papers

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Goodness-of-fit tests for quantile regression with missing responses

et al. 2019

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Hypothesis test on response mean with inequality constraints under data missing when covariables are present

Cited by 6 publications

References 16 publications

Weighted empirical likelihood for heteroscedastic varying coefficient partially non‐linear models with missing data

Weighted empirical likelihood for heteroscedastic varying coefficient partially non‐linear models with missing data

CLT for integrated square error of density estimators with censoring indicators missing at random

Goodness-of-fit tests for quantile regression with missing responses

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