Within forest growth modeling it is customary to employ LOGIT models to predict individual tree mortality. In this paper we use Learning Vector Quantization and the self-organizing map as diierent formalisms to predict individual tree mortality. The data set for this study came from permanent sample plots in uneven-aged Norway spruce (Picea abies L. Karst) stands in Austria. After parameterizin...

In this paper we employ Mecklenburg County, North Carolina, a polycentric city with 10 employment subcenters, as a case study to explore the role of employment subcenters in determining residential location decisions. We estimate discrete choice models of residential location decisions: conditional logit models and heteroscedastic logit models with both the full choice set and sampled choices. ...

Using discrete choice experiments (DCEs) to estimate health state utility values has become an important alternative to the conventional methods of Time Trade-Off and Standard Gamble. Studies using DCEs have typically used the conditional logit to estimate the underlying utility function. The conditional logit is known for several limitations. In this paper, we propose two types of models based...

This paper estimates a locational choice model to assess the demand for local public services, using a data set where individuals chooses between 26 municipalities within a local labor market. We assess the importance of the IIA assumption by comparing the predictions of three difference models; the conditional logit (CL) model, the mixed logit (MXL) model, and the multinomial probit (MNP) mode...

Maximum entropy models are often used to describe supply and demand behavior in urban transportation and land use systems. However, they have been criticized for not representing behavioral rules of system agents and because their parameters seems to adjust only to modeler-imposed constraints. In response, it is demonstrated that the solution to the entropy maximization problem with linear cons...

Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the author...

In this paper we examine theoretically and by simulation whether or not unobserved heterogeneity independent of the included regressors is really an issue in logit, probit and loglog models with both binary and fractional data. We found that unobserved heterogeneity: (i) produces an attenuation bias in the estimation of regression coefficients; (ii) is innocuous for logit estimation of average ...

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...