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Penalized Likelihood-type Estimators for Generalized Nonparametric Regression

Author

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  • Cox, Dennis D.
  • O'Sullivan, Finbarr

Abstract

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.

Suggested Citation

  • Cox, Dennis D. & O'Sullivan, Finbarr, 1996. "Penalized Likelihood-type Estimators for Generalized Nonparametric Regression," Journal of Multivariate Analysis, Elsevier, vol. 56(2), pages 185-206, February.
    • Handle: RePEc:eee:jmvana:v:56:y:1996:i:2:p:185-206
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    Cited by:

    1. Daniel Commenges & Benoit Liquet & Cécile Proust-Lima, 2012. "Choice of Prognostic Estimators in Joint Models by Estimating Differences of Expected Conditional Kullback–Leibler Risks," Biometrics, The International Biometric Society, vol. 68(2), pages 380-387, June.

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