## Bayesian Analysis

### Nonparametric estimation of the distribution function in contingent valuation models

#### 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.

#### Article information

Source
Bayesian Anal., Volume 4, Number 3 (2009), 573-597.

Dates
First available in Project Euclid: 22 June 2012

https://projecteuclid.org/euclid.ba/1340369855

Digital Object Identifier
doi:10.1214/09-BA421

Mathematical Reviews number (MathSciNet)
MR2551046

Zentralblatt MATH identifier
1330.62428

#### Citation

Leslie, David S.; Kohn, Robert; Fiebig, Denzil G. Nonparametric estimation of the distribution function in contingent valuation models. Bayesian Anal. 4 (2009), no. 3, 573--597. doi:10.1214/09-BA421. https://projecteuclid.org/euclid.ba/1340369855

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