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Model averaging estimation for nonparametric varying-coefficient models with multiplicative heteroscedasticity

Author

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  • Xianwen Sun

    (Zhejiang University)

  • Lixin Zhang

    (Zhejiang University)

Abstract

In the last few years, frequentist model averaging has received more and more attention. However, the majority of related work focuses on the parametric model setup and devotes more energy to different mean structures but ignores the form of variance. In this paper, we consider a regression model with the mean being a varying-coefficient model and the variance being multiplicative heteroscedasticity and introduce a model averaging approach that uses the B-spline smoothing method to estimate unknown coefficient functions and obtains the estimators of unknown parameters in both the mean and variance functions of the model by the maximum likelihood method. The resulting model averaging estimator is proved to have asymptotic optimality under some regular conditions. Simulation experiments are conducted to compare the performance of our method with that of other common heteroscedasticity-robust model selection and model averaging methods under the finite-sample case. Our method is also verified in a real dataset.

Suggested Citation

  • Xianwen Sun & Lixin Zhang, 2024. "Model averaging estimation for nonparametric varying-coefficient models with multiplicative heteroscedasticity," Statistical Papers, Springer, vol. 65(3), pages 1375-1409, May.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:3:d:10.1007_s00362-023-01447-8
    DOI: 10.1007/s00362-023-01447-8
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