SemiParSampleSel-package: Semiparametric Sample Selection Modelling with Continuous Response
Description
SemiParSampleSel provides a function for fitting continuous response (copula) sample selection models
with parametric and nonparametric predictor effects.Details
SemiParSampleSel provides a function for flexible sample selection modelling with continuous (normal or gamma) response. The underlying
representation and
estimation of the model is based on a penalized regression spline approach, with automatic smoothness selection. The numerical routine carries
out function minimization using a trust region algorithm from the package trust in combination
with an adaptation of a low level smoothness selection fitting procedure from the package mgcv, via a `leapfrog' algorithm.
SemiParSampleSel supports the use of many smoothers as extracted from mgcv. Scale invariant tensor product smooths
are not currently supported. Estimation is by penalized maximum likelihood with automatic smoothness selection by approximate Un-Biased
Risk Estimator (UBRE) score.
The depedence between the selection and outcome equations is modelled through the use of copulas.
Confidence intervals for smooth components are derived using a Bayesian approach. P-values for testing
individual smooth terms for equality to the zero function are also provided. Functions plot.SemiParSampleSel and
summary.SemiParSampleSel extract such information from a fitted SemiParSampleSelObject. Variable selection is also
possible via the use of shrinakge smoothers or information criteria.References
Marra G. and Radice R. (in press), Estimation of a Regression Spline Sample Selection Model. Computational Statistics and Data Analysis.
Wojtys M., Marra G. and Radice R. (submitted), Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel.