Nonparametric multiplicative heteroscedasticity in multi-dimensional regression
2017
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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