Smooth backfitting in generalized additive models

Details Smooth backfitting in generalized additive models Smooth backfitting in generalized additive models

2008-03-13

arxiv.org/abs/0803.1922v1

Annals of Statistics 2008, Vol. 36, No. 1, 228-260

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/009053607000000596 the Annals of Statistics (http://www.imstat.org/aos/) by the Inst

Scientific paper

10.1214/009053607000000596

Generalized additive models have been popular among statisticians and data analysts in multivariate nonparametric regression with non-Gaussian responses including binary and count data. In this paper, a new likelihood approach for fitting generalized additive models is proposed. It aims to maximize a smoothed likelihood. The additive functions are estimated by solving a system of nonlinear integral equations. An iterative algorithm based on smooth backfitting is developed from the Newton--Kantorovich theorem. Asymptotic properties of the estimator and convergence of the algorithm are discussed. It is shown that our proposal based on local linear fit achieves the same bias and variance as the oracle estimator that uses knowledge of the other components. Numerical comparison with the recently proposed two-stage estimator [Ann. Statist. 32 (2004) 2412--2443] is also made.

Affiliated with

Also associated with

No associations

Rating

Smooth backfitting in generalized additive models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Smooth backfitting in generalized additive models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Smooth backfitting in generalized additive models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-69759