lclogit2: An enhanced command to fit latent class conditional logit models

Conditional Logit Models

Predictive Random Fields: Latent Variable Models Fit by Multiway Conditional Probability with Applications to Document Analysis

We introduce predictive random fields, a framework for learning undirected graphical models based not on joint, generative likelihood, or on conditional likelihood, but based on a product of several conditional likelihoods each relying on common sets of parameters and predicting different subsets of variables conditioned on other subsets. When applied to models with latent variables, such as th...

Latent Class Models for Marketing: An Application to Pharmaceuticals

In this paper some extensions of the latent class (LC) approach are applied to analyze the Italian pharmaceutical market. This sector is characterized by a high level of competitiveness, more limited economic budgets than years ago and, at the same time, expensive sales and promotion activities; in this context, it is very important to know the reference market, so to design appropriate marketi...

Latent class CUB models

The paper proposes a latent class version of CUB models for ordinal data to account for unobserved heterogeneity. The extension, called LC-CUB, is useful when the heterogeneity is originated by clusters of respondents not identified by covariates: this may generate a multimodal response distribution, which cannot be adequately described by a standard CUB model. The LC-CUBmodel is a finite mixtu...

Latent Class Models

Over the past several years more significant books have been published on latent class and other types of finite mixture models than any other class of statistical models. The recent increase in interest in latent class models is due to the development of extended computer algorithms, which allow today's computers to perform latent class analysis on data containing more than just a few variable...