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Title:Approximation of bayesian efficiency in experimental choice designs PDF Logo
Authors:Bliemer, Michiel C. J.
Rose, John M.
Hess, Stephane
Issue Date:2008
Citation:[Journal:] Journal of Choice Modelling [ISSN:] 1755-5345 [Volume:] 1 [Year:] 2008 [Issue:] 1 [Pages:] 98-127
Abstract:This paper compares different types of simulated draws over a range of number of draws in generating Bayesian efficient designs for stated choice (SC) studies. The paper examines how closely pseudo Monte Carlo, quasi Monte Carlo and Gaussian quadrature methods are able to replicate the true levels of Bayesian efficiency for SC designs of various dimensions. The authors conclude that the predominantly employed method of using pseudo Monte Carlo draws is unlikely to result in leading to truly Bayesian efficient SC designs. The quasi Monte Carlo methods analysed here (Halton, Sobol, and Modifed Latin Hypercube Sampling) all clearly outperform the pseudo Monte Carlo draws. However, the Gaussian quadrature method examined in this paper, incremental Gaussian quadrature, outperforms all, and is therefore the recommended approximation method for the calculation of Bayesian efficiency of SC designs.
Subjects:experimental design
Bayesian efficiency
(quasi) Monte Carlo simulation
Gaussian quadrature
Creative Commons License:http://creativecommons.org/licenses/by-nc/2.0/uk/
Document Type:Article
Appears in Collections:Journal of Choice Modelling

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