Statistical inference of partially linear regression models with heteroscedastic errors

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 nonp...

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Veröffentlicht in:Journal of multivariate analysis 2007-09, Vol.98 (8), p.1539-1557
Hauptverfasser: You, Jinhong, Chen, Gemai, Zhou, Yong
Format: Artikel
Sprache:eng
Schlagworte:
Asymptotic normality
Errors
Estimating techniques
Exact sciences and technology
Functional analysis
Functions of a complex variable
Heteroscedasticity
Linear inference, regression
Local polynomial
Mathematical analysis
Mathematics
Model selection
Multivariate analysis
Probability and statistics
Regression analysis
Sciences and techniques of general use
Semiparametric regression model
Semiparametric regression model Heteroscedasticity Local polynomial Asymptotic normality Model selection
Statistics
Studies
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Beschreibung
Zusammenfassung: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.
ISSN:0047-259X
1095-7243
DOI:10.1016/j.jmva.2007.06.011