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A nonparametric Bayesian method for estimating a response function

Scott Brown and Glen Meeden

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Abstract

Consider the problem of estimating a response function which depends upon a non-stochastic independent variable under our control. The data are independent Bernoulli random variables where the probabilities of success are given by the response function at the chosen values of the independent variable. Here we present a nonparametric Bayesian method for estimating the response function. The only prior information assumed is that the response function can be well approximated by a mixture of step functions.

Chapter information

Source
Dominique Fourdrinier, Éric Marchand and Andrew L. Rukhin, eds., Contemporary Developments in Bayesian Analysis and Statistical Decision Theory: A Festschrift for William E. Strawderman (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2012), 190-199

Dates
First available in Project Euclid: 14 March 2012

Permanent link to this document
https://projecteuclid.org/euclid.imsc/1331731620

Digital Object Identifier
doi:10.1214/11-IMSCOLL813

Mathematical Reviews number (MathSciNet)
MR3202511

Zentralblatt MATH identifier
1326.62016

Subjects
Primary: 62C10: Bayesian problems; characterization of Bayes procedures 62C15: Admissibility
Secondary: 62G05: Estimation

Keywords
binary regression nonparametric Bayes stepwise Bayes

Rights
Copyright © 2012, Institute of Mathematical Statistics

Citation

Brown, Scott; Meeden, Glen. A nonparametric Bayesian method for estimating a response function. Contemporary Developments in Bayesian Analysis and Statistical Decision Theory: A Festschrift for William E. Strawderman, 190--199, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2012. doi:10.1214/11-IMSCOLL813. https://projecteuclid.org/euclid.imsc/1331731620


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