logit models are one of the most important discrete choice models and they play an important role in describing decision makers’ choices among alternatives. in this paper the multi-nominal logit models has been used in mode choice modeling of isfahan. despite the availability of different mathematical computer programs there are not so many programs available for estimating discrete choice mode...

The goal of this study is to identify the risk factors of type II diabetes (T2D) in Al Madinah in the Kingdom of Saudi Arabia (KSA). Data were collected from the Primary Health Care Center (PHCC) of Al Madinah, KSA, from 2009 to 2014. The data are grouped or summarized, where the patients have different types of diabetes. The dependent variable is the type of T2D, and the independent variables ...

This article presents a general approach for logit random effects modelling of clustered ordinal and nominal responses. We review multinomial logit random effects models in a unified form as multivariate generalized linear mixed models. Maximum likelihood estimation utilizes adaptive Gauss–Hermite quadrature within a quasi-Newton maximization algorithm. For cases in which this is computationall...

breast cancer is a malignant tumor and the most common cancer among american women that starts from cells of the breast. the growth of the size of data exceeds the ability of humans to analyze the data. so we need some intelligent machines that can analyze datasets intelligently for us. we propose a multi nominal logit model to evaluate correlation between risk factors of breast tumor. our mode...

Breast cancer is a malignant tumor and the most common cancer among American women that starts from cells of the breast. The growth of the size of data exceeds the ability of humans to analyze the data. So we need some intelligent machines that can analyze datasets intelligently for us. We propose a Multi Nominal Logit Model to evaluate correlation between risk factors of breast tumor. Our ...

-This paper presents a financial distress prediction model that combines the approaches of neural network learning and logit analysis. This combination can retain the advantages and avoid the disadvantages of the two kinds of approaches in solving such a problem. The radial basis function network (RBFN) is adopted to construct the prediction model. The architecture of RBFN allows the grouping o...

Social scientists, particularly political scientists, frequently use ordinal survey items as dependent variables in models of political attitudes. Commonly, normal-theory modeling strategies like ordinary least squares regression are applied to these items. Additionally, workers also make frequent use of the proportional odds (ordinal logit) model or cumulative probit model when working with su...

Multinomial logit models which are most commonly used for the modeling of unordered multi-category responses are typically restricted to the use of few predictors. In the high-dimensional case maximum likelihood estimates frequently do not exist. In this paper we are developing a boosting technique called multinomBoost that performs variable selection and fits the multinomial logit model also w...