Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain. Unfortunately, to date, it is unable to handle huge feature spaces typical of CRM applications. Hence, the analyst is forced to immerse himself into fe...

the presence of bubbles in the markets and its formation has been regarded by economists and they have been looking to develop methods that can be recognized by using appropriate method for the formation of bubbles. in this paper, first, the formation of bubbles is tested using the new unit root test known as phillips test (generalized sup adf test) for 50 companies in the tehran stock exchange...

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Modeling binary and categorical data is one of the most commonly encountered tasks applied statisticians econometricians. While Bayesian methods in this context have been available for decades now, they often require a high level familiarity with statistics or suffer from issues such as low sampling efficiency. To contribute to accessibility models data, we introduce novel latent variable repre...

Empirical data and statistical models are used to answer the question of where the new highway routes are most likely to be located. High-quality land-use, population distribution and highway network GIS data for the Twin Cities Metropolitan Area from 1958 to 1990 are developed for this study. The highway system is classified into three levels, Interstate highways, divided highways, and seconda...

Most previous studies of binary choice panel data models with Þxed effects require strictly exogeneous regressors, and except for the logit model without lagged dependent variables, cannot provide rate root n parameter estimates. We assume that one of the explanatory variables is independent of the individual speciÞc effect and of the errors of the model, conditional on the other explanatory va...

As shown by Guimaraes, Figueiredo and Woodward (2003), a particular class of conditional logit models yield identical parameter estimates to a Poisson count data model. In Schmidheiny and Brülhart (2011), we have pointed out that the conditional logit model and the Poisson model can be seen as polar cases of a continuum of intermediate cases which emerge from a random utility nested logit model...

This paper studies posterior contraction rates in multi-category logit models with priors incorporating group sparse structures. We consider a general class of that includes the well-known multinomial as special case. Group sparsity is useful when predictor variables are naturally clustered and particularly for variable selection models. provide unified platform group-sparse include binary logi...

This study aims to investigate the effects of chronic diseases and socio-economic factors on demand for family medicine. The basic approach used is Andersen's behavioral health model. variables in analysis were obtained from “TurkStat Health Survey” micro data set 2016. Three models established determine degree disease affecting demand. Binary Logit regression was models. such as gender, age, e...