Logistic regression is the primary analysis tool for binary traits in genome-wide association studies (GWAS). Multinomial regression extends logistic regression to multiple categories. However, many ...

A generic random effects formulation for the Dirichlet negative multinomial distribution is developed together with a convenient regression parameterization. A ...

Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R with "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations in R ...

Abstract: Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis ...

Abstract: In this paper, we present multinomial latent logistic regression (MLLR), a new learning paradigm that introduces latent variables to logistic regression. By inheriting the advantages of ...

We consider the problem of inference for a multinomial regression model. The sampling distribution of responses for this model, and, in turn, its likelihood, may be represented exactly by a certain ...

Paper aims This paper addresses the influence of socioeconomic, quality, built environment, and safety variables on the demand for public transportation service. Originality This study covers a ...