Penalized Likelihood-type Estimators for Generalized Nonparametric Regression
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.
Year of publication: |
1996
|
---|---|
Authors: | Cox, Dennis D. ; O'Sullivan, Finbarr |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 56.1996, 2, p. 185-206
|
Publisher: |
Elsevier |
Keywords: | maximum penalized likelihood non-parametric aggression multiple classification smoothing splines rates of convergence (null) |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Inverse decision theory with medical applications
Davies, Kalatu R., (2005)
-
Zhang, Nan, (2010)
-
Statistical models for intraday trading dynamics
Bhatti, Chad Reyhan, (2007)
- More ...