bprobgHsSS: Internal Function
Description
It provides the log-likelihood, gradient and observed or expected information matrix for
penalized or unpenalized maximum likelihood optimization, for the non-random sample selection model case. 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.References
Marra G. and Radice R. (2013), A Penalized Likelihood Estimation Approach to Semiparametric Sample Selection Binary Response Modeling. Electronic Journal of Statistics, 7, 1432-1455.
McGovern M.E., Barnighausen T., Marra G. and Radice R. (2015), On the Assumption of Joint Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence. Epidemiology, 26(2), 229-237.