Locally Adaptive Nonparametric Binary Regression

2007/09/21 by Sally Wood, Wood, Sally, Robert Kohn +8
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference #stat.ME

paper · pdf · doi:10.48550/arxiv.0709.3545

31 pages, 10 figures

arxiv created 2007/09/21 · openalex publication_date 2007/09/21 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Local Regression and Likelihood
• Hierarchical Mixtures of Experts and the EM Algorithm
• Multivariate Adaptive Regression Splines
• Bayesian Analysis of Binary and Polychotomous Response Data
• Adaptive Mixtures of Local Experts

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