The following pages link to SemiParBIVProbit (Q20179):
Displaying 13 items.
- Estimation of a regression spline sample selection model (Q333716) (← links)
- Copula regression spline models for binary outcomes (Q340845) (← links)
- Sample selection models for count data in R (Q722737) (← links)
- A joint regression modeling framework for analyzing bivariate binary data in \(\mathsf{R}\) (Q1697000) (← links)
- The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification (Q1740276) (← links)
- A penalized likelihood estimation approach to semiparametric sample selection binary response modeling (Q1951164) (← links)
- Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models (Q2259749) (← links)
- On \(p\)-values for semiparametric bivariate probit models (Q2360906) (← links)
- Identification in a generalization of bivariate probit models with dummy endogenous regressors (Q2397724) (← links)
- Multivariate effect priors in bivariate semiparametric recursive Gaussian models (Q2416770) (← links)
- Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach (Q2628886) (← links)
- Estimation of a semiparametric recursive bivariate probit model with nonparametric mixing (Q2802853) (← links)
- Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity (Q3087590) (← links)
