This study focuses on accommodating spatial dependency in data indexed by geographic
location. In particular, the emphasis is on accommodating spatial error correlation across …

This study proposes a simple and practical Composite Marginal Likelihood (CML) inference
approach to estimate ordered-response discrete choice models with flexible copula-based …

The current paper focuses on analyzing and modeling the physical activity participation
levels (in terms of the number of daily “bouts” or “episodes” of physical activity during a …

This paper presents a modeling methodology capable of accounting for spatial correlation
across choice alternatives in discrete choice modeling applications. Many location choice …

The dominant approach in the literature to dealing with sample selection is to assume a
bivariate normality assumption directly on the error terms, or on transformed error terms, in …

The spatial correlation, or colocation, of two or more variables is a fundamental issue in
geographical analysis but has received much less attention than the spatial correlation of …

Non-Gaussian spatial data are common in many fields. When fitting regressions for such
data, one needs to account for spatial dependence to ensure reliable inference for the …

We propose a new copula model for replicated multivariate spatial data. Unlike classical
models that assume multivariate normality of the data, the proposed copula is based on the …

The discrete choice modeling paradigm and in particular the logit model are research topics
that have been continuously developed and refined for years in the field of transportation …

The paper presents a basic spatial discrete choice modeling framework obtained by
applying random utility theory to discrete choices made by heterogeneous spatially …