We use the method of maximum likelihood and regression splines to derive estimates of the parametric and nonparametric components of semiparametric generalized linear models. The resulting estimators of both components are shown to be consistent. Also, the asymptotic theory for the estimator of the parametric component is derived, indicating that the parametric component can be estimated efficiently without under-smoothing the nonparametric component.
Publié le : 1995-08-14
Classification:
Partial spline model,
generalized linear model,
semiparametric regression model,
maximum likelihood estimator,
62G07,
62F12,
62J12
@article{1176324700,
author = {Chen, Hung},
title = {Asymptotically Efficient Estimation in Semiparametric Generalized Linear Models},
journal = {Ann. Statist.},
volume = {23},
number = {6},
year = {1995},
pages = { 1102-1129},
language = {en},
url = {http://dml.mathdoc.fr/item/1176324700}
}
Chen, Hung. Asymptotically Efficient Estimation in Semiparametric Generalized Linear Models. Ann. Statist., Tome 23 (1995) no. 6, pp. 1102-1129. http://gdmltest.u-ga.fr/item/1176324700/