Evaluation of a new mixed logit (MIXL) estimator using Monte Carlo simultations


The random parameter logit (RPL) or mixed logit (MIXL) model is the most commonly used model to estimate respondents' preferences obtained using discrete choice experiments (DCEs). However, currently available estimators either produce unreliable results or are prohibitively slow when used with larger datasets, which warrants investigations into a more reliable and faster approach to analyzing large-scale DCEs. A promising candidate would be an estimator based on Laplace Approximations. In this application, I request the required computation time to establish the relative performance of Laplace Approximations versus existing MIXL estimators using the Cartesius computing facilities.





Dr. M.F. Jonker

Verbonden aan

Erasmus Universiteit Rotterdam, Erasmus MC, Maatschappelijke Gezondheidszorg


01/12/2017 tot 01/01/2018