Locally Adaptive Nonparametric Binary Regression
Methodology
2007-09-25 v1
Abstract
A nonparametric and locally adaptive Bayesian estimator is proposed for estimating a binary regression. Flexibility is obtained by modeling the binary regression as a mixture of probit regressions with the argument of each probit regression having a thin plate spline prior with its own smoothing parameter and with the mixture weights depending on the covariates. The estimator is compared to a single spline estimator and to a recently proposed locally adaptive estimator. The methodology is illustrated by applying it to both simulated and real examples.
Cite
@article{arxiv.0709.3545,
title = {Locally Adaptive Nonparametric Binary Regression},
author = {Sally Wood and Robert Kohn and Remy Cottet and Wenxin Jiang and Martin Tanner},
journal= {arXiv preprint arXiv:0709.3545},
year = {2007}
}
Comments
31 pages, 10 figures