Locally adaptive nonparametric binary regression

Sally A. Wood, Robert Kohn*, Remy Cottet, Wenxin Jiang, Martin Tanner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We propose a nonparametric and locally adaptive Bayesian estimator 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.

Original languageEnglish (US)
Pages (from-to)352-372
Number of pages21
JournalJournal of Computational and Graphical Statistics
Volume17
Issue number2
DOIs
StatePublished - Jun 1 2008

Keywords

  • Bayesian analysis
  • Markov Chain Monte Carlo
  • Mixture-of-experts
  • Model averaging
  • Reversible jump
  • Surface estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Locally adaptive nonparametric binary regression'. Together they form a unique fingerprint.

Cite this