Locally Adaptive Nonparametric Binary Regression

Simple item page
dc.creatorWood, Sally
dc.creatorKohn, Robert
dc.creatorCottet, Remy
dc.creatorJiang, Wenxin
dc.creatorTanner, Martin
dc.date2007-09-21
dc.date.accessioned2026-07-07T08:31:39Z
dc.date.available2026-07-07T08:31:39Z
dc.descriptionA 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.
dc.description31 pages, 10 figures
dc.identifierhttps://arxiv.org/abs/0709.3545
dc.identifierhttp://arxiv.org/abs/0709.3545
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/138564
dc.subjectMethodology
dc.titleLocally Adaptive Nonparametric Binary Regression
dc.typetext

Files

Collections

arXiv