Probit ("probability unit") regression is a classical machine learning technique that can be used for binary classification -- predicting an outcome that can only be one of two discrete values. For ...

Probit regression is very similar to logistic regression and the two techniques typically give similar results. Probit regression tends to be used most often with finance and economics data, but both ...

This is a preview. Log in through your library . Abstract Quantal bioassay experiments relate the amount or potency of some compound; for example, poison, antibody, or drug to a binary outcome such as ...

Equicorrelated binary observations are modelled using a multivariate probit regression model. Log likelihood derivatives are reduced to simple linear combinations of equicorrelated multivariate normal ...

The GLM procedure fits general linear models to data, and it can perform regression, analysis of variance, analysis of covariance, and many other analyses. The following features for regression ...

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, ...

Some of you may have come across a growing number of publications in your field using an alternative paradigm called Bayesian statistics in which to perform their statistical analyses. The goal of ...