Results, and Conclusions: 1) Logit and probit transformations are often successfully used to mimic a linear model. Logistic regression, Cox regression, Poisson regression, and Markow modeling are ...

Multivariate binary data arise in a variety of settings. In this article we propose a practical and efficient computational framework for maximum likelihood estimation of multivariate probit ...

Lee C. Spector, Michael Mazzeo, Probit Analysis and Economic Education, The Journal of Economic Education, Vol. 11, No. 2 (Spring, 1980), pp. 37-44 ...

The probit regression model is a model used to analyze the relationship between categorical response variables, with predictive variables that are numerical, categorical, or the combination of both.

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, ...

We introduce a new class of methods and inference techniques for infinite mixtures of Inverse Gaussian, Multinomial Probit and Exponential Regression, models that belong to the widely applicable ...

The LOGISTIC and PROBIT procedures can perform logistic and ordinal logistic regression. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," Chapter 39, "The LOGISTIC Procedure," ...