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June, 1984 Bayesian Nonparametric Inference for Quantal Response Data
Larry P. Ammann
Ann. Statist. 12(2): 636-645 (June, 1984). DOI: 10.1214/aos/1176346511

Abstract

The problem of nonparametric Bayes estimation of a tolerance distribution based on quantal response data has been considered previously with a prior distribution based on the Dirichlet process. In the present article, a broad class of priors is developed for this problem by allowing the hazard function of the tolerance distribution to be a realization of a nonnegative stochastic process with independent increments. This class includes the Dirichlet prior as a special case. In addition, priors over a space of absolutely continuous tolerance distributions, which includes IFR, DFR, and U-shaped failure rate distributions, are constructed by taking the failure rate to be the superposition of two processes with independent increments. Posterior Laplace transforms of the corresponding processes are obtained based on quantal response data with binomial sampling. These posterior Laplace transforms are then used to find Bayes estimates, and examples are given to illustrated the results.

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Larry P. Ammann. "Bayesian Nonparametric Inference for Quantal Response Data." Ann. Statist. 12 (2) 636 - 645, June, 1984. https://doi.org/10.1214/aos/1176346511

Information

Published: June, 1984
First available in Project Euclid: 12 April 2007

zbMATH: 0576.62051
MathSciNet: MR740917
Digital Object Identifier: 10.1214/aos/1176346511

Subjects:
Primary: 62E20
Secondary: 62G99

Rights: Copyright © 1984 Institute of Mathematical Statistics

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Vol.12 • No. 2 • June, 1984
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