This paper compares neural network models with the standard logit and probit models, the most widely used choice/classi cation models in current empirical research, and explores the application of neural network models in analyzing political choice/classi cation problems. Political relationships are usually nonlinear and of unknown functional forms, and political data are likely noisy. The logi...

This chapter examines different models commonly used to model probabilistic choice, such as eg the choice of one type of transportation from among many choices available to the consumer. Section 1 discusses derivation and limitations of conditional logit models. Section 2 discusses probit models and Section 3 discusses the nested logit (generalized extreme value models), which address some of t...

Pregibit: A Family of Discrete Choice Models The pregibit discrete choice model is built on a distribution that allows symmetry or asymmetry and thick tails, thin tails or no tails. Thus the model is much richer than the traditional models that are typically used to study behavior that generates discrete choice outcomes. Pregibit nests logit, approximately nests probit, loglog, cloglog and goss...

aim : the aim of this study was to determine whether there is relation between body mass index and symptoms of gastro-esophageal reflux disease in our community using logit, probit and complementary log-log models. background : the most frequent statistical tool to address the relationship among a dichotomous response and other covariates is logistic regression. however logistic regression is f...

Logit kernel is a discrete choice model that has both probit-like disturbances as well as an additive i.i.d. extreme value (or Gumbel) disturbance à la multinomial logit. The result is an intuitive, practical, and powerful model that combines the flexibility of probit with the tractability of logit. For this reason, logit kernel has been deemed the “model of the future” and is becoming extremel...

Probit residuals need not sum to zero in general. However, if explanatory variables are qualitative the sum can be shown to be zero for many models. Indeed this remains true for binary dependent variable models other than Probit and Logit. Even if some explanatory variables are quantitative, residuals can sum to almost zero more often than might at first seem plausible.

Elimination by aspects (EBA) is a random utility model that is considered to represent the choice process used by consumers more faithfully than logit and probit models. One limitation of the model is that it does not have a known error theory. We show that EBA can be derived by assuming that aspects have random utilities with independent, extreme value distributions. Multinomial logit and rank...

We discuss some empirical and methodological issues arising when the Cox test statistic for non-nested models is used to test the probit vs. logit specifications. As an example, we consider the models in Bardasi and Monfardini (1997) for the occupational choice by Italian workers among the private, public and self-employed options. Different versions of the test are compared. The bootstrap tech...

conclusions independent variables such as sex, age, hdl-c, sys-bp, bmi, smoking, cholesterol and hs-crp were considered as influencing variables, and education, marital status, disease history, height, waist circumference, diastolic blood pressure and low density lipoprotein were removed from the models. background cardiovascular disease is considered as one of the most common diseases, causing...