A comparison of corporate failure models in Australia: Hybrid neural networks, logit models and discriminant analysis. A comparison of corporate failure models in Australia: Hybrid neural networks, logit models and discriminant analysis. Abstract This study investigated whether two artificial neural networks (ANNs), multilayer perceptron (MLP) and hybrid networks using statistical and ANN appro...

Current opinion regarding the selection of link function in binary response models is that the probit and logit links give essentially similar results. This seems to be true for univariate binary response models; however, for multivariate binary response models such advice is misleading. We address a gap in the literature by empirically examining the relationship between link function selection...

Many behavioral and nonbehavioral models have been proposed to model pedestrian behavior. Among these, pedestrians' decision processes have not been explicitly modeled. Utility-maximizing models have been prominently used, but these models may be misspecified owing to their unrealistic assumptions. As an alternative, this paper proposes cut-off models based on the satisficing heuristic founded ...

BACKGROUND Laparoscopic surgery for common bile duct stones varies procedurally from a transcystic approach to laparoscopic choledochotomy (LC) with or without biliary drainage. However, LC is a difficult procedure with higher documented morbidity than the transcystic approach. We retrospectively investigated risk factors for adverse outcomes of LC. METHODS We used logistic regression models ...

mlogit is a package for R which enables the estimation the multinomial logit models with individual and/or alternative specific variables. The main extensions of the basic multinomial model (heteroscedastic, nested and random parameter models) are implemented.

mlogit is a package for R which enables the estimation of the multinomial logit models with individual and/or alternative specific variables. The main extensions of the basic multinomial model (heteroscedastic, nested and random parameter models) are implemented.

In this paper, we propose nonlinear distance-odds models investigating elevated odds around point sources of exposure, under a matched case-control design where there are subtypes within cases. We consider models analogous to the polychotomous logit models and adjacent-category logit models for categorical outcomes and extend them to the nonlinear distance-odds context. We consider multiple poi...