Jackknife Model Averaging for Composite Quantile Regression

Jackknife Model Averaging for Composite Quantile Regression

In this paper, the authors propose a frequentist model averaging method for composite quantile regression with diverging number of parameters. Different from the traditional model averaging for quantile regression which considers only a single quantile, the proposed model averaging estimator is base...

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Veröffentlicht in:Journal of systems science and complexity 2024, Vol.37 (4), p.1604-1637
Hauptverfasser: You, Kang, Wang, Miaomiao, Zou, Guohua
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Sprache:eng
Schlagworte:
Complex Systems
Control
Error analysis
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Quantiles
Regression models
Statistics
Systems Theory
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Wang, Miaomiao
Zou, Guohua
description In this paper, the authors propose a frequentist model averaging method for composite quantile regression with diverging number of parameters. Different from the traditional model averaging for quantile regression which considers only a single quantile, the proposed model averaging estimator is based on multiple quantiles. The well-known delete-one cross-validation or jackknife approach is applied to estimate the model weights. The resultant jackknife model averaging estimator is shown to be asymptotically optimal in terms of minimizing the out-of-sample composite final prediction error. Simulation studies are conducted to demonstrate the finite sample performance of the new model averaging estimator. The proposed method is also applied to the analysis of the stock returns data and the wage data.
doi_str_mv 10.1007/s11424-024-2448-1
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subjects Complex Systems
Control
Error analysis
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Quantiles
Regression models
Statistics
Systems Theory
title Jackknife Model Averaging for Composite Quantile Regression
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