The implications of Salisbury and Feinberg's (2010) paper [Salisbury, L. C., F. M. Feinberg. 2010. Alleviating the constant stochastic variance assumption in decision research: Theory, measurement, and experimental test. (1) 1–17] for the process of model development and testing in the field...

We develop a Markov Chain Monte Carlo (MCMC) algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparameterization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples...

Choice modelling (CM) is a developing non-market valuation technique that provides a rich data set to the analyst. In addition to parameter estimates for the influence of attributes, labels and respondent characteristics, models also provide information about, (and are sensitive to), the error...