Nonparametric estimation of the distribution function in contingent valuation models

Leslie, David S. and Kohn, Robert and Fiebig, Denzil G. (2009) Nonparametric estimation of the distribution function in contingent valuation models. Bayesian Analysis, 4 (3). pp. 573-598. ISSN 1936-0975

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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 coe±cients ¯xed. A method for °exibly estimating the link func- tion 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 sam- pling 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.

Item Type: Journal Article
Journal or Publication Title: Bayesian Analysis
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2600/2604
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 70756
Deposited By: ep_importer_pure
Deposited On: 12 Sep 2014 08:30
Refereed?: Yes
Published?: Published
Last Modified: 01 Jan 2020 08:58
URI: https://eprints.lancs.ac.uk/id/eprint/70756

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