Substantive income effects are incorporated in a logit or nested-logit model by assuming that utility is a piece-wise linear spline function of residual income. Specific income data are not required, only income by category. Expected compensating variation is easily and accurately approximated by the difference between expected maximum utility in the proposed and initial state, multiplied by th...

One of the challenging issues for investors and professionals is appropriate models to evaluate financial situation of the firms. In this regard, many models have been extracted by researchers using different financial ratios to resolve these issues. However, choosing a model based on the conditions and users’ needs is complex. The main objective of this study is to identify the effect of conti...

The aim of this study was a comparison of risk stratification for death in patients after myocardial infarction (MI) and of risk stratification for malignant arrhythmias in patients with implantable cardioverter-defibrillator (ICD). The individual risk factors and more complex approaches were used, which take into account that a borderline between a risky and non-risky value of each predictor i...

according to the limit of resources in the subject of prioritization, one of the alternative methods is mcdm method. generally, mcdm models have been developed under certainty while we confront with under uncertainty in real world. in hierarchical mcdm methods, one of the main steps is to weigh criteria and computes each alternative weight using defined criteria in the next steps. one of the ea...

In this paper we develop a two-step survival time approach – a discrete logit model with survival time dummies – that allows for time-varying explanatory variables and interval censored data. Our empirical analysis reveals that the twostep approach outperforms the benchmark logit model with respect to out-ofsample prediction accuracy. Survival time, however, does not play an important role. The...

Ecologists increasingly rely on Bayesian methods to fit capture–recapture models. Capture–recapture models are used estimate abundance while accounting for imperfect detectability in individual-level data. A variety of implementations exist such models, including integrated likelihood, parameter-expanded data augmentation, and combinations those. with latent random effects can be computationall...