Assortment optimization problems arise widely in many practical applications such as retailing and online advertising. In this problem, the goal is to select a subset from a universe of substitutable items to offer to customers in order to maximize the expected revenue. The demand of any item depends on the substitution behavior of the customers that is captured mathematically by a choice model...

The multinomial probit (MNP) model is a primary application for combining simulation with estimation. Indeed, McFadden (1989) featured the MNP model in his seminal paper. As random utility model, the MNP model offers a highly desirable flexibility in substitution among alternatives that its chief rival, the multinomial logit model, fails to possess. The unrestricted character of the variance ma...

In this paper, we present a Weibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log–log) can be obtained as limiting cases. We further compare the proposed model with some other asymmetrical models. The Bayesian as well as fre...

An ex ante adoption model of bovine somatotropin (BST) is estimated with survey data of California milk producers. Theoretical justification is developed for incorporation of socioeconomic explanatory variables in a technology-adoption model. The advantages of a multinomial over a binomial ex ante model also are presented. The multinomial logit model is used to predict BST adoption, to test hyp...

This paper deals with the correspondence between macro and micro spatial interaction and choice models, in both a static and a dynamic context. After a review of recent literature, special attention is devoted to the interpretation of aggregate spatial interaction models (both the conventional gravity or entropy models and the more general theory of movement devëloped by Alonso) in the framewor...

We compare multinomial logit and mixed logit models for data on California households’ revealed and stated preferences for automobiles. The stated preference data elicited households’ preferences among gas, electric, methanol, and CNG vehicles with various attributes. The mixed logit models provide a much better fit to these data, and forecasting exercises demonstrate substantial differences be...

Title: WEATHER IMPACT ON ROAD ACCIDENT SEVERITY IN MARYLAND YUE LIU, M.S. in Civil and Environmental Engineering, 2013 Directed By: Professor and Chair Ali Haghani, Department of Civil and Environmental Engineering This study was conducted to analyze and quantify the impact of weather factors on road accident severity, based on Maryland accident data during 2007-2010. In order to find a better ...

Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the author...

Multiple linear regression with special properties of its coefficients parameterized by exponent, logit, and multinomial functions is considered. To obtain always positive coefficients the exponential parameterization is applied. To get coefficients in an assigned range, the logistic parameterization is used. Such coefficients permit us to evaluate the impact of individual predictors in the mod...