Approximation of bayesian efficiency in experimental choice designs

By Michiel C. J. Bliemer, John M. Rose, Stephane Hess. 2008

232 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/183c3191b489ca7e17351ed3d1a5ba6b18088113

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In Computational Theory and Mathematics

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Construction of experimental designs for mixed logit models allowing for correlation across choice observations (2010, methodology): “Gaussian quadrature with 5 abscissas is being used (see e.g., Bliemer et al., 2008) for simulating the probabilities in the mixed logit models, and an additional sample of 500 respondents is generated (using Halton draws for the EV1 error terms in the utility functions) for computing the panel…”
Efficient stated choice experiments for estimating nested logit models (2009, methodology): “…Bayesian efficient designs is much more computationally intensive than generating efficient designs given fixed priors, as the expected D-error can only be computed through simulated integration (see Bliemer et al., 2006), using similar techniques as in mixed logit models (see e.g., Train, 2003).”
Spatial Heterogeneity of Willingness to Pay for Forest Management (2017, methodology): “The experimental design was optimized for Bayesian (median) d-efficiency of the MXL model (Sándor and Wedel 2001; Ferrini and Scarpa 2007; Bliemer et al. 2008; Scarpa and Rose 2008). To obtain initial estimates (priors) and verify the qualitative properties of the questionnaire itself, we conducted ”
Choice experiment assessment of public preferences for forest structural attributes (2015, unknown): “The choice sets employed in our study were prepared using a Bayesian d‐efficient design optimized for MNL (Ferrini et al., 2007; Bliemer et al., 2008).”
Bayesian optimal designs for discrete choice experiments with partial profiles (2011, methodology): “Bliemer et al. (2008) and Yu et al. (2010) showed that quadrature methods often outperform any other methods for Bayesian objective function evaluation.”