Generalized Nonparametric Regression via Penalized Likelihood
نویسندگان
چکیده
We consider the asymptotic analysis of penalized likelihood type estimators for generalized non-parametric 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. Upper bounds on rates of convergence for penalized likelihood-type estimators are obtained by approximating estimators in terms of one-step Taylor series expansions. AMS 1980 subject classifications. Primary, 62-G05, Secondary, 62J05, 41-A35, 41-A25, 47-A53, 45-LlO, 45-M05.
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