Multivariate probit model

This article is about modeling several correlated binary outcomes. For modeling a single event with multiple outcomes, see multinomial probit.
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Background

In statistics and econometrics, the multivariate probit model is a generalization of the probit model used to estimate several correlated binary outcomes jointly. For example, if it is believed that the decisions of sending at least one child to public school and that of voting in favor of a school budget are correlated (both decisions are binary), then the multivariate probit model would be appropriate for jointly predicting these two choices on an individual-specific basis. J.R. Ashford and R.R. Sowden initially proposed an approach for multivariate probit analysis.[1] Siddhartha Chib and Edward Greenberg extended this idea and also proposed simulation-based inference methods for the multivariate probit model which simplified and generalized parameter estimation.[2]

  1. ^ Ashford, J.R.; Sowden, R.R. (September 1970). "Multivariate Probit Analysis". Biometrics. 26 (3): 535–546. doi:10.2307/2529107. JSTOR 2529107. PMID 5480663.
  2. ^ Chib, Siddhartha; Greenberg, Edward (June 1998). "Analysis of multivariate probit models". Biometrika. 85 (2): 347–361. CiteSeerX 10.1.1.198.8541. doi:10.1093/biomet/85.2.347 – via Oxford Academic.