The generalized nested logit model is a new member of the generalized extreme value family of models. The GNL provides a higher degree of flexibility in the estimation of substitution or cross-elasticity between pairs of alternatives than previously developed generalized extreme value models. The generalized nested logit model includes the paired combinatorial logit and cross-nested logit model...

This paper discusses the representability of discrete logit-type models including multinomial logit and nested logit model from a mathematical approach. It is shown that the logit-type models can be reconstructed from mathematical approximation theory with sigmoidal functions widely used in Neural Network modeling without the basic assumptions such as IIA and iid, and the distribution (or densi...

This paper investigates the application of a nested logit model to urban transit networks where every set of competitive transit lines is described by a particular graph structure called hyperpath. It shows that the nested logit model transcends the inherent limitations of the simple logit model while retaining the algorithmic advantages similar to that obtained with the ordinary logit model fo...

Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and...

While there is growing application of generalized ordered outcome model variants (widely known as Generalized Ordered Logit (GOL) model and Partial Proportional Odds Logit (PPO) model) in crash injury severity analysis, there are several aspects of these approaches that are not well documented in extant safety literature. The current research note presents the relationship between these two var...

The Multinomial Logit, discrete choice model of transport demand, has several restrictions when compared with the more general Multinomial Probit model. The most famous of these are that unobservable components of utilities should be mutually independent and homoskedastic. Correlation can be accommodated to a certain extent by the Hierarchical Logit model, but the problem of heteroskedasticity ...