In this research, we propose a novel approach for a multinomial logit model selection procedure: specifically, we apply association rules analysis to identifying potential interactions for multinomial logit modeling. Interaction effects are very common in reality, but conventional multinomial logit model selection methods typically ignore them. This is especially true for higher-order interacti...

The paper considers panel data methods for estimating ordered logit models with individual-specific correlated unobserved heterogeneity. We show that a popular approach is inconsistent, whereas some consistent and efficient estimators are available, including minimum distance and generalized method-of-moment estimators. A Monte Carlo study reveals the good properties of an alternative estimator...

In this paper we focus on the relationship between the error rate which is one of the representative reliability measures in Apache web servers and the system parameters which reflect on the web server's system performance, and develop a probability model to describe it. More specifically, we implement a simple client server system and carry out an experiment to measure both the error rate and ...

Unless the true association is very strong, simple large-sample confidence intervals for the odds ratio based on the delta method perform well even for small samples. Such intervals include the Woolf logit interval and the related Gart interval based on adding .5 before computing the log odds ratio estimate and its standard error. The Gart interval smooths the observed counts toward the model o...

The mixed logit model is considered to be the most promising state of the art discrete choice model currently available. Increasingly researchers and practitioners are estimating mixed logit models of various degrees of sophistication with mixtures of revealed preference and stated choice data. It is timely to review progress in model estimation since the learning curve is steep and the unwary ...

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...

Generalized Logit models of demand systems for energy and other factors have been shown to work well in comparison with other popular models, such as the Almost Ideal Demand System and the TransLog model. The main reason is that the derived price elasticities are robust when expenditure shares are small, as they are for electricity and fuels. A number of different versions of the Generalized Lo...

This paper uses data on automobile purchases to compare two alternatives to the multinomial logit model. The results indicate that the nested logit model is likely to be superior to the more general Berry–Levinsohn–Pakes model for applications using aggregate-level data.  2000 Elsevier Science S.A. All rights reserved.

objective: poverty alleviation is an essential step to achieve economic development. this is why identification exercise is so crucial. traditionally, different aspects of insecurities have not be taken into poverty measurement. many types of insecurities have adverse impacts on household welfare. identifying vulnerability of household can serve identification of the poor households. methodolog...

Functional logistic regression has been developed to forecast a binary response variable from a functional predictor. In order to fit this model, it is usual to assume that the functional observations and the parameter function of the model belong to a same finite space generated by a basis of functions. This consideration turns the functional model into a multiple logit model whose design matr...