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Nonparametric estimation of an additive model with a link function

Authors: Joel L. Horowitz and Enno Mammen
Date: 01 December 2004
Type: Journal Article, Annals of Statistics, Vol. 32, No. 6, pp. 2412-2443


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.

Previous version:
Joel L. Horowitz and Enno Mammen July 2002, Nonparametric estimation of an additive model with a link function, cemmap Working Paper, CWP19/02, IFS

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