The logit and probit models have become critical parts of the management researcher's analytical arsenal, growing rapidly from almost no use in the 1980s to appearing in 15% of all articles published ...

We consider optimal designs for a class of symmetric models for binary data which includes the common probit and logit models. We show that for a large group of optimality criteria which includes the ...

The LOGISTIC, GENMOD, PROBIT, and CATMOD procedures can all be used for statistical modeling of categorical data. The CATMOD procedure provides maximum likelihood estimation for logistic regression, ...

Abstract Ordinal outcome neural networks represent an innovative and robust methodology for analyzing high-dimensional health data characterized by ordinal outcomes. This study offers a comparative ...

Aldrich, J.H. and Nelson, F.D. (1984) Linear Probability, Logit and Probit Model Quantitative Application in the Social Science-Sera Miller McCun. Sage pub. Inc, University of Minnesota and Iola, ...

We compare how logit (fixed effects) and probit early warning systems (EWS) predict insample and out-of-sample currency crises in emerging markets (EMs). We look at episodes of currency crises that ...

A benefit because probit modeling (and its close cousin logit modeling) can be customized in an endless variety to extract estimates of a particular state from raw data.

We, in contrast, discuss the health outcome including self-rated health status, incidence rates of related diseases, ADL, IADL, and overweight according to certain standards through multiple ...