Sampling of Alternatives in Random Regret Minimization Models (presentation)
Angelo Guevara, Caspar G. Chorus, Moshe E Ben-Akiva · Data Archiving and Networked Services (DANS) · 2013
We propose a methodology to achieve consistency, asymptotic normality and efficiency, while sampling alternatives in Random Regret Minimization models. Our method is an extension of previous results for Logit and MEV models. We illustrate the methodology using Monte Carlo experimentation. Experiments show that the proposed methodology