Statistical inference of partially linear regression models with heteroscedastic errors
The authors study a heteroscedastic partially linear regression model and develop an inferential procedure for it. This includes a test of heteroscedasticity, a two-step estimator of the heteroscedastic variance function, semiparametric generalized least-squares estimators of the parametric and nonparametric components of the model, and a bootstrap goodness of fit test to see whether the nonparametric component can be parametrized.
Year of publication: |
2007
|
---|---|
Authors: | You, Jinhong ; Chen, Gemai ; Zhou, Yong |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 98.2007, 8, p. 1539-1557
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Publisher: |
Elsevier |
Keywords: | Semiparametric regression model Heteroscedasticity Local polynomial Asymptotic normality Model selection |
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