Nonparametric estimation of an additive model with a link function

Joel L. Horowitz*, Enno Mammen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

This paper describes an estimator of the additive components of a nonparametric additive model with a known link function. When the additive components are twice continuously differentiable, the estimator is asymptotically normally distributed with a rate of convergence in probability of n -2/5. This is true regardless of the (finite) dimension of the explanatory variable. Thus, in contrast to the existing asymptotically normal estimator, the new estimator has no curse of dimensionality. Moreover, the estimator has an oracle property. The asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.

Original languageEnglish (US)
Pages (from-to)2412-2443
Number of pages32
JournalAnnals of Statistics
Volume32
Issue number6
DOIs
StatePublished - Dec 2004

Keywords

  • Additive models
  • Kernel estimates
  • Multivariate curve estimation
  • Nonparametric regression
  • Orthogonal series estimator

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Nonparametric estimation of an additive model with a link function'. Together they form a unique fingerprint.

Cite this