bprobgHs: Internal Function
Description
It provides the log-likelihood, gradient and observed information matrix for
penalized or unpenalized maximum likelihood optimization. Possible bivariate distributions are bivariate normal, Clayton, rotated Clayton (90 degrees), survival Clayton, rotated Clayton (270 degrees), Joe, rotated Joe (90 degrees), survival Joe, rotated Joe (270 degrees), Gumbel, rotated Gumbel (90 degrees), survival Gumbel, rotated Gumbel (270 degrees), and Frank. For the normal case only, the Fisher information is also available.References
Marra G. and Radice R. (2011), Estimation of a Semiparametric Recursive Bivariate Probit in the Presence of Endogeneity. Canadian Journal of Statistics, 39(2), 259-279.
Radice R., Marra G. and M. Wojtys (submitted), Copula Regression Spline Models for Binary Outcomes with Application in Health Care Utilization.