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2009 Nonparametric estimation of the distribution function in contingent valuation models
Denzil G. Fiebig, Robert Kohn, David S. Leslie
Bayesian Anal. 4(3): 573-597 (2009). DOI: 10.1214/09-BA421

Abstract

Contingent valuation models are used in Economics to value non-market goods and can be expressed as binary choice regression models with one of the regression coefficients fixed. A method for flexibly estimating the link function of such binary choice model is proposed by using a Dirichlet process mixture prior on the space of all latent variable distributions, instead of the more restricted distributions in earlier papers. The model is estimated using a novel MCMC sampling scheme that avoids the high autocorrelations in the iterates that usually arise when sampling latent variables that are mixtures. The method allows for variable selection and is illustrated using simulated and real data.

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Denzil G. Fiebig. Robert Kohn. David S. Leslie. "Nonparametric estimation of the distribution function in contingent valuation models." Bayesian Anal. 4 (3) 573 - 597, 2009. https://doi.org/10.1214/09-BA421

Information

Published: 2009
First available in Project Euclid: 22 June 2012

zbMATH: 1330.62428
MathSciNet: MR2551046
Digital Object Identifier: 10.1214/09-BA421

Rights: Copyright © 2009 International Society for Bayesian Analysis

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