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arXiv · 0709.3545

Locally Adaptive Nonparametric Binary Regression

Sally Wood·Robert Kohn·Remy Cottet·Wenxin Jiang·Martin Tanner
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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.

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Sally Wood, Robert Kohn, Remy Cottet, Wenxin Jiang, Martin Tanner. 2007-09-21. Locally Adaptive Nonparametric Binary Regression. https://arxiv.org/abs/0709.3545

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