validate scoring of legal borrowers in banks (using logit and probit models)

Probit and nested logit models based on fuzzy measure

Inspired by the interactive discrete choice logit models [Aggarwal, 2019], this paper presents the advanced families of discrete choice models, such as nested logit, mixed logit, and probit models to consider the interaction among the attributes. Besides the DM's attitudinal character is also taken into consideration in the computation of choice probabilities. The proposed choice models make us...

I nteraction terms in logit and probit models

The magnitude of the interaction effect in nonlinear models does not equal the marginal effect of the interaction term, can be of opposite sign, and its statistical significance is not calculated by standard software. We present the correct way to estimate the magnitude and standard errors of the interaction effect in nonlinear models.  2003 Elsevier Science B.V. All rights reserved.

Probit and Logit Models: Differences in the Multivariate Realm

Current opinion regarding the selection of link function in binary response models is that the probit and logit links give essentially similar results. This seems to be true for univariate binary response models; however, for multivariate binary response models such advice is misleading. We address a gap in the literature by empirically examining the relationship between link function selection...

Group comparisons in logit and probit using predicted probabilities1

The comparison of groups in regression models for binary outcomes is complicated by an identification problem inherent in these models. Traditional tests of the equality of coefficients across groups confound the magnitude of the regression coefficients with residual variation. If the amount of residual variation differs between groups, the test can lead to incorrect conclusions (Allison 1999)....

Relaxing the IIA Assumption in Locational Choice Models: A Comparison Between Conditional Logit, Mixed Logit, and Multinomial Probit Models∗

This paper estimates a locational choice model to assess the demand for local public services, using a data set where individuals chooses between 26 municipalities within a local labor market. We assess the importance of the IIA assumption by comparing the predictions of three difference models; the conditional logit (CL) model, the mixed logit (MXL) model, and the multinomial probit (MNP) mode...

Using Heterogeneous Choice Models To Compare Logit and Probit Coefficients Across Groups