Abstract
In many statistical applications, the variability of the data is an important issue. For instance, in the regression analysis, researchers often meet the heteroscedasticity problem. There is a wide body of literature about the nonparametric estimation of the conditional variance function in one-dimensional case. However there are only few papers about the nonparametric estimation of the conditional variance function when there are several regressors in the model. In this paper, we propose a smooth backfitting estimator for the multiplicative conditional variance function and study the asymptotic property and finite sample performance via simulation studies.
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Yu, K. Nonparametric multiplicative heteroscedasticity in multi-dimensional regression. J. Korean Stat. Soc. 46, 404–412 (2017). https://doi.org/10.1016/j.jkss.2017.01.001
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DOI: https://doi.org/10.1016/j.jkss.2017.01.001