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Delete-group Jackknife Estimate in Partially Linear Regression Models with Heteroscedasticity
- Source :
- Acta Mathematicae Applicatae Sinica, English Series. 19:599-610
- Publication Year :
- 2003
- Publisher :
- Springer Science and Business Media LLC, 2003.
-
Abstract
- Consider a partially linear regression model with an unknown vector parameter β, an unknown function g(·), and unknown heteroscedastic error variances. Chen, You[23] proposed a semiparametric generalized least squares estimator (SGLSE) for β, which takes the heteroscedasticity into account to increase efficiency. For inference based on this SGLSE, it is necessary to construct a consistent estimator for its asymptotic covariance matrix. However, when there exists within-group correlation, the traditional delta method and the delete-1 jackknife estimation fail to offer such a consistent estimator. In this paper, by deleting grouped partial residuals a delete-group jackknife method is examined. It is shown that the delete-group jackknife method indeed can provide a consistent estimator for the asymptotic covariance matrix in the presence of within-group correlations. This result is an extension of that in [21].
Details
- ISSN :
- 16183932 and 01689673
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- Acta Mathematicae Applicatae Sinica, English Series
- Accession number :
- edsair.doi.dedup.....58f75a700511abd14694e0c330502968