clogithet fits a heteroscedastic version of McFadden's conditional logit model. This model is
also referred to as the parametrised heteroscedastic multinomial logit model (Hensher et al …

When the sample size is small compared to the number of cells in a contingency table,
maximum likelihood estimates of logit parameters and their associated standard errors may …

Using maximum likelihood (ML) estimation for discrete choice modeling of small datasets
causes two problems. The first problem is that the data may exhibit separation, in which case …

Latent class models have recently drawn considerable attention among many researchers
and practitioners as a class of useful tools for capturing heterogeneity across different …

Multinomial logit models are used to model relationships between a polytomous response
variable and a set of regressor variables. The term “multinomial logit model” includes, in a …

Abstract Latent Class Choice Models (LCCM) are extensions of discrete choice models
(DCMs) that capture unobserved heterogeneity in the choice process by segmenting the …

Rchoice is a package in R for estimating models with individual heterogeneity for both cross-
sectional and panel (longitudinal) data. In particular, the package allows binary, ordinal and …

We present a summary of important computational issues and opportunities that arise from
the use of semi-aggregate data (where the explanatory data for choice scenarios are not …

A random-coefficients logit model that allows for unobserved heterogeneity in brand
preferences and in the responses to marketing variables is empirically investigated using …