Abstract Generalized linear mixed models form a general class of random effects models for discrete and continuous response in the exponential family. Spatial GLMM are an extension of such models that allows us to fit spatial-dependent data. A popular model in this class is the probit-normal model. In this study we develop a novel exact algorithm to estimate a probit spatial generalized linear ...

Data for discrete ordered random variables are often characterised by “excessive” zero observations. Traditional ordered probit models have limited capacity in explaining the preponderance of zero observations, especially when the zeros may relate to two distinct situations of non-participation and infrequent participation (or consumption), for example. We propose a zero-inflated ordered probit...

This study investigates the politico-economic determinants of the crowding-in effects of public investments in a cross-section of 25 developing countries for the 1975-2000 period using multivariate probit analysis. The estimation results show that public fixed capital investments may crowd in private investments, but this still depends on the developments in the economic, political, and legal e...

This study relies on data collected by the National Elections Studies project at the University of Michigan. Any errors in interpretation or analysis are the responsibility of the authors.

This paper presents a formulation of the multiple user class, variable demand, probit stochastic user equilibrium model (SUE). Sufficient conditions are stated for differentiability of the equilibrium flows of this model. This justifies the derivation of sensitivity expressions for the equilibrium flows. This paper then considers the network design problem (NDP), assuming that users’ responses ...

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...

Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial ordered probit (DSOP) model in order to capture...

High-amplitude, MV/m, nanosecond pulsed electric fields (nsPEF) have been hypothesized to cause nanoporation of the plasma membrane. Phosphatidylserine (PS) externalization has been observed on the outer leaflet of the membrane shortly after nsPEF exposure, suggesting local structural changes in the membrane. In this study, we utilized fluorescently-tagged Annexin V to observe the externalizati...

Generalized linear mixed models (GLMM) form a very general class of random effects models for discrete and continuous responses in the exponential family. They are useful in a variety of applications. The traditional likelihood approach for GLMM usually involves high dimensional integrations which are computationally intensive. In this work, we investigate the case of binary outcomes analyzed u...