Accuracy is often analyzed using analysis of variance techniques in which the data are assumed to be normally distributed. However, accuracy data are discrete rather than continuous, and proportion correct are constrained to the range 0–1. Monte Carlo simulations are presented illustrating how this can lead to distortions in the pattern of means. An alternative is to analyze accuracy using logi...

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...

In statistics, a multinomial logit (MNL) model, also known as multinomial logistic regression, is a regression model which generalizes logistic regression by allowing more than two discrete outcomes.[1] That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which...

Despite the ordinal nature of Self-Rated Health (SRH) variable, logistic regression models or regression models have been used without adequate justification for these applications. It is shown that ordered-logit regression model is the appropriate statistical strategy to estimate SRH, whereas the Ordinary LeastSquares model leads to biased conclusions.

This study analyzes characteristics determining member’s willingness to invest in cooperatives using ordered logit model. The data were collected in a field survey among 122 cooperative members in north-central China. The descriptive analysis of survey evidence suggests that cooperatives in China generally having poor ability to deliver the processing services related to product package, gradin...

We address function misspecification and model heterogeneity, two critical issues in empirical work. A nonparametric approach is proposed for single-index, binary-choice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without knowing the conditional pro...