The development of accurate models and efficient algorithms for the analysis of multivariate categorical data are important and longstanding problems in machine learning and computational statistics. In this paper, we focus on modeling categorical data using Latent Gaussian Models (LGMs). We propose a novel stick-breaking likelihood function for categorical LGMs that exploits accurate linear an...

Binary response regression is a useful technique for analyzing categorical data. Popular binary models use special link functions such as the logit or the probit link. In this article, the inverse link function H is modeled to be a scale mixture of cumulative distribution functions. Two different models for H are proposed: (i) H is a finite normal scale mixture with a Dirichlet distribution pri...

In this study, the manner of private taxis drivers has been investigated for choosing passenger and destination from a fixed point. Therefore, two models called Multinomial and Nested Logit Models have been utilized. The information gained by scrolling in 2016 is the input data, which are in the format of revealed preference, acquired by the verbal interview in Vanak Square in Tehran (Iran). Ba...

probabilistic approach of traffic assignment has been primarily developed to provide a more realistic and flexible theoretical framework to represent traveler’s route choice behavior in a transportation network. the problem of path overlapping in network modelling has been one of the main issues to be tackled. due to its flexible covariance structure, probit model can adequately address the pro...

This paper estimates ordered logit and probit regression models for bank ratings which also include a country index to capture country-specific variation. The empirical findings provide support to the hypothesis that the individual international bank ratings assigned by Fitch Ratings are underpinned by fundamental quantitative financial analyses. Also, there is strong evidence of a country effe...

This paper presents a forecasting approach, in which stock price direction in the next day can be predicted based on nonlinear probability models and technical indicators. The proposed method incorporates various indicators into Logit, Probit, and Extreme Value models permitting a decision maker to forecast the direction of stock movements more efficiently. The utilized indicators include Movin...

This paper considers the determinants of civil con‡ict, using a zero-in‡ated Poisson modelling approach that deals with the problem of excess zero observations, which we argue are related to two distinct data generation processes. Despite their continued use in the literature, traditional probit and logit models have limited capacity in dealing with this issue and can lead to misleading results...

F several of the largest supermarket product categories, such as carbonated soft drinks, canned soups, ready-to-eat cereals, and cookies, consumers regularly purchase assortments of products. Within the category, consumers often purchase multiple products and multiple units of each alternative selected on a given trip. This multiple discreteness violates the single-unit purchase assumption of m...