Nonparametric estimation of an additive model with a link function

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 asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.

• Publication year

  2002

• Venue

  Annals of Statistics

• Publication date

  2002-07-13

• Fields of study

  Mathematics

• Identifiers
  DOI 10.1214/009053604000000814arXiv math/0508595
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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