This paper presents the process of derivation and development of a spatial multinomial logit model and its application to a housing type choice problem. Over the past few years, a relatively small body of research was developed that tries to capture the spatial and temporal dependencies across decision-makers and alternatives. While temporal dependencies are often considered especially in dynam...

The nested logit model has been used extensively to model multi-dimensional choice situations. A drawback of the nested logit model is that it does not allow choice alternatives to share common unobserved attributes along all the dimensions characterizing the multi-dimensional choice context. This paper formulates a mixed multinomial logit structure that accommodates unobserved correlation acro...

Classical methods for fitting a varying intercept logistic regression model to stratified data are based on the conditional likelihood principle to eliminate the stratum-specific nuisance parameters. When the outcome variable has multiple ordered categories, a natural choice for the outcome model is a stratified proportional odds or cumulative logit model. However, classical conditioning techni...

Mobile ad hoc networks (MANETs) have been utilized to execute many applications in diverse environments. Trust is an effective mechanism to cope with misbehaving nodes. However, implementing trust in MANETs is confronted by several obstacles, i.e., no centralized authority, dynamic environments, and limited observations which hinder trust accuracy. In this work, we propose a novel logit regress...

Background: Novel models for the assessment of non-linear data are being developed for the benefit of making better predictions from the data. Objective: To review traditional and modern models. Results, and Conclusions: 1) Logit and probit transformations are often successfully used to mimic a linear model. Logistic regression, Cox regression, Poisson regression, and Markow modeling are exampl...

In this paper estimators for distribution free heteroskedastic binary response models are proposed. The estimation procedures are based on relationships between distribution free models with a conditional median restriction and parametric models (such as Probit/Logit) exhibiting (multiplicative) heteroskedasticity. The first proposed estimator is based on the observational equivalence between t...

As shown by Guimaraes, Figueiredo and Woodward (2003), a particular class of conditional logit models yield identical parameter estimates to a Poisson count data model. In Schmidheiny and Brülhart (2011), we have pointed out that the conditional logit model and the Poisson model can be seen as polar cases of a continuum of intermediate cases which emerge from a random utility nested logit model...