A number of authors have discussed the possible advantages of conditioning parameter
distributions on observed choices when working with Mixed Multinomial Logit models …

The simulated choice probabilities in mixed logit models are usually approximated
numerically using Halton or random draws from a multivariate mixing distribution for the …

The" workhorse" model for analysing discrete choice data, the conditional logit model, can
be implemented in Stata using the official clogit and asclogit commands. While widely used …

Within the realm of logit-type random parameter models to address unobserved
heterogeneity in preferences there are two dominant approaches: the mixed logit model …

With the gains in popularity of random coefficients models such as Mixed Logit, an
increasing body of work is looking at the issue of the distributional assumptions made in …

The logit-mixed logit (LML) model, which allows the analyst to semi-parametrically specify
the mixing distribution of preference heterogeneity, is a very recent advancement in logit …

The mixed logit model is considered to be the most promising state of the art discrete choice
model currently available. Increasingly researchers and practitioners are estimating mixed …

Maximum likelihood and Bayesian estimation are both frequently used to fit mixed logit
models to choice data. The type and the number of quasi-random draws used for simulating …

The random coefficients multinomial choice logit model, also known as the mixed logit, has
been widely used in empirical choice analysis for the last thirty years. We prove that the …

This paper develops nonparametric estimation for discrete choice models based on the
mixed multinomial logit (MMNL) model. It has been shown that MMNL models encompass …