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Penalized Likelihood-type Estimators for Generalized Nonparametric Regression

✍ Scribed by Dennis D. Cox; Finbarr O'Sullivan

Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
861 KB
Volume
56
Category
Article
ISSN
0047-259X

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✦ Synopsis

We consider the asymptotic analysis of penalized likelihood type estimators for generalized nonparametric regression problems in which the target parameter is a vector-valued function defined in terms of the conditional distribution of a response given a set of covariates. A variety of examples including ones related to generalized linear models and robust smoothing are covered by the theory. Linear approximations to the estimator are constructed using Taylor expansions in Hilbert spaces. An application which is treated is upper bounds on rates of convergence for the penalized likelihood-type estimators.

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