A spatial latent class analysis model that extends the classic latent class analysis model by adding spatial structure to the latent class distribution through the use of the multinomial probit model is introduced. Linear combinations of independent Gaussian spatial processes are used to develop multivariate spatial processes that are underlying the categorical latent classes. This allows the l...

In this article, we propose a generalized estimating equations (GEE) approach for correlated ordinal or nominal multinomial responses using a local odds ratios parameterization. Our motivation lies upon observing that: (i) modeling the dependence between correlated multinomial responses via the local odds ratios is meaningful both for ordinal and nominal response scales and (ii) ordinary GEE me...

We show that the distributions of random coefficients in various discrete choice models are nonparametrically identified. Our identification results apply to static discrete choice models including binary logit, multinomial logit, nested logit, and probit models as well as to dynamic programming discrete choice models. In these models the only key condition we need to verify for identification ...

This paper examines the relationship between senior management team culture and organizational performance in English acute hospitals (NHS Trusts) over three time periods between 2001/2002 and 2007/2008. We use a validated culture rating instrument, the Competing Values Framework, to measure senior management team culture. Organizational performance is assessed using a wide range of routinely c...

Categorical distributions are ubiquitous in machine learning, e.g., in classification, language models, and recommendation systems. They are also at the core of discrete choice models. However, when the number of possible outcomes is very large, using categorical distributions becomes computationally expensive, as the complexity scales linearly with the number of outcomes. To address this probl...

We develop a new Gibbs sampler for a linear mixed model with a Dirichlet process random effect term, which is easily extended to a generalized linear mixed model with a probit link function. Our Gibbs sampler exploits the properties of the multinomial and Dirichlet distributions, and is shown to be an improvement, in terms of operator norm and efficiency, over other commonly used MCMC algorithm...

This study examines the relationship between teleworking, gender roles and happiness of couples using data from the British Household Panel Survey (BHPS) and the Understanding Society Survey (USS) during the period 1991-2012. Various approaches are followed, including Probit-adapted fixed effects, multinomial Logit and Instrumental variables (IV). The results support that both men and women who...

Marketers often analyze multinomial choice from a set of branded products to learn about demand. Given a set of brands to study, we analyze three reasons why choices from strict subsets of the brands can contain more statistical information about demand than choices from all the brands in the study: First, making choices from smaller subsets is easier, so it is possible to use more choice-tasks...

This article introduces a generalization of Tanner and Wong’s data augmentation algorithm which can be used when the complete data posterior distribution cannot be directly sampled. The algorithm proposes parameter values based on complete data sampling distributions of convenient frequentist estimators which ignore some information in the complete data likelihood. The proposals are filtered us...