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Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel

Marra, G; Wojtys, M; Radice, R; (2016) Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel. Journal of Statistical Software , 71 (6) 10.18637/jss.v071.i06. Green open access

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Abstract

Sample selection models deal with the situation in which an outcome of interest is observed for a restricted non-randomly selected sample of the population. The estimation of these models is based on a binary equation, which describes the selection process, and an outcome equation, which is used to examine the substantive question of interest. Classic sample selection models assume a priori that continuous covariates have a linear or pre-specified non-linear relationship to the outcome, and that the distribution linking the two equations is bivariate normal. We introduce the R package SemiParSampleSel which implements copula regression spline sample selection models. The proposed implementation can deal with non-random sample selection, non-linear covariate-response relationships, and non-normal bivariate distributions between the model equations. We provide details of the model and algorithm and describe the implementation in SemiParSampleSel. The package is illustrated using simulated and real data examples.

Type: Article
Title: Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel
Open access status: An open access version is available from UCL Discovery
DOI: 10.18637/jss.v071.i06
Publisher version: http://dx.doi.org/10.18637/jss.v071.i06
Language: English
Additional information: This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA. This licence allows for copying any part of the work for personal and commercial use, providing author attribution is clearly stated.
Keywords: copula, non-random sample selection, penalized regression spline, selection bias, R
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1476058
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