Copula based generalized additive models for location, scale and shape with non-random sample selection
dc.contributor.author | Wojtys, Malgorzata | |
dc.contributor.author | Marra, G | |
dc.contributor.author | Radice, R | |
dc.date.accessioned | 2018-05-23T12:28:04Z | |
dc.date.issued | 2018-11 | |
dc.identifier.issn | 0167-9473 | |
dc.identifier.issn | 1872-7352 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/11561 | |
dc.description.abstract |
Non-random sample selection is a commonplace amongst many empirical studies and it appears when an output variable of interest is available only for a restricted non-random sub-sample of data. An extension of the generalized additive models for location, scale and shape which accounts for non-random sample selection by introducing a selection equation is discussed. The proposed approach allows for potentially any parametric distribution for the outcome variable, any parametric link function for the selection equation, several dependence structures between the (outcome and selection) equations through the use of copulae, and various types of covariate effects. Using a special case of the proposed model, it is shown how the score equations are corrected for the bias deriving from non-random sample selection. Parameter estimation is carried out within a penalized likelihood based framework. The empirical effectiveness of the approach is demonstrated through a simulation study and a case study. The models can be easily employed via the gjrm function in the R package GJRM. | |
dc.format.extent | 1-14 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Elsevier | |
dc.subject | Additive predictor | |
dc.subject | Copula | |
dc.subject | Marginal distribution | |
dc.subject | Non-random sample selection | |
dc.subject | Penalized regression spline | |
dc.subject | Simultaneous equation estimation | |
dc.title | Copula based generalized additive models for location, scale and shape with non-random sample selection | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000439748700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.volume | 127 | |
plymouth.publication-status | Published | |
plymouth.journal | Computational Statistics and Data Analysis | |
dc.identifier.doi | 10.1016/j.csda.2018.05.001 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dcterms.dateAccepted | 2018-05-02 | |
dc.rights.embargodate | 2019-5-12 | |
dc.identifier.eissn | 1872-7352 | |
dc.rights.embargoperiod | 12 months | |
rioxxterms.versionofrecord | 10.1016/j.csda.2018.05.001 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved | |
rioxxterms.licenseref.startdate | 2018-11 | |
rioxxterms.type | Journal Article/Review |