This paper reports on the development of a learning system for the prediction of dichotomous response variables by combining fuzzy concept with classical regression technique. The algorithm involves linear transformation followed by linear programming. In the algorithm presented it was assumed that the logarithm of the odds (logit) is linearly related to X’s, the independent variables after und...

The assortment optimization problem under the mixture of multinomial logit models is NPcomplete and there are different approximation methods to obtain upper bounds on the optimal expected revenue. In this paper, we analytically compare the upper bounds obtained by the different approximation methods. We propose a new, tractable approach to construct an upper bound on the optimal expected reven...

this study predicts corporate bankruptcy five years before its occurrence using financial ratios introduced in altman’s z-score model and current ratio. three estimation methods namely; liner probability, logit and probit models have chosen for model estimation. the sample contains 134 companies in tehran stock exchange during 2003. the precision of prediction of the estimated models for the ma...

the paper examines industrial concentration in iranian food products and beverages industries using firm level data aggregated to the 4-digit isic industry level between 2002 and 2004. based on different concentration indices the average level of concentration has increased slightly for the period of study. the empirical results show that increase in the level of concentration is more likely in...

Neural networks have proved in many ways and in a number of publications to be real challengers to statistical methods especially to logit analysis in predicting failures. However, most of the studies have used a rather small data set, very often close to only one hundred observations. Therefore, it has been difficult to say whether there are any significant differences between the methods test...

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...

We show that the distributions of random coefficients in various discrete choice models are nonparametrically identified. Our identification results apply to static discrete choice models including binary logit, multinomial logit, nested logit, and probit models as well as to dynamic programming discrete choice models. In these models the only key condition we need to verify for identification ...

As datasets capturing human choices grow in richness and scale—particularly in online domains—there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce’s choice axiom. In this work we introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, an inferentially tractable model that does not ...

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...