Imputation-based empirical likelihood inferences for partially nonlinear quantile regression models with missing responses

Xiaoshuang Zhou; Peixin Zhao; Yujie Gai

doi:10.1007/s10182-022-00441-z

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Imputation-based empirical likelihood inferences for partially nonlinear quantile regression models with missing responses

Xiaoshuang Zhou Peixin Zhao Yujie Gai

Year: 2022 Journal: AStA Advances in Statistical Analysis Vol: 106 (4)Pages: 705-722 Publisher: Springer Science+Business Media

DOI: 10.1007/s10182-022-00441-z

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Keywords:

Imputation (statistics) Quantile regression Econometrics Empirical likelihood Missing data Statistics Quantile Mathematics Nonlinear regression Computer science Regression analysis Estimator

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Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Advanced Statistical Methods and Models

Physical Sciences → Mathematics → Statistics and Probability

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Imputation-based empirical likelihood inferences for partially nonlinear quantile regression models with missing responses

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