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http://hdl.handle.net/1942/25163
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DC Field | Value | Language |
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dc.contributor.author | Barthel, Nicole | - |
dc.contributor.author | GEERDENS, Candida | - |
dc.contributor.author | Killiches, Matthias | - |
dc.contributor.author | JANSSEN, Paul | - |
dc.contributor.author | Czado, Claudia | - |
dc.date.accessioned | 2017-11-13T11:28:13Z | - |
dc.date.available | 2017-11-13T11:28:13Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | COMPUTATIONAL STATISTICS & DATA ANALYSIS, 117, p. 109-127 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | http://hdl.handle.net/1942/25163 | - |
dc.description.abstract | In many studies multivariate event time data are generated from clusters having a possibly complex association pattern. Flexible models are needed to capture this dependence. Vine copulas serve this purpose. Inference methods for vine copulas are available for complete data. Event time data, however, are often subject to right-censoring. As a consequence, the existing inferential tools, e.g. likelihood estimation, need to be adapted. A two-stage estimation approach is proposed. First, the marginal distributions are modeled. Second, the dependence structure modeled by a vine copula is estimated via likelihood maximization. Due to the right-censoring single and double integrals show up in the copula likelihood expression such that numerical integration is needed for its evaluation. For the dependence modeling a sequential estimation approach that facilitates the computational challenges of the likelihood optimization is provided. A three-dimensional simulation study provides evidence for the good finite sample performance of the proposed method. Using four-dimensional mastitis data, it is shown how an appropriate vine copula model can be selected for data at hand. | - |
dc.description.sponsorship | Parts of the numerical calculations were performed on a Linux cluster supported by DFG grant INST 95/919-1 FUGG. Funding: This work was supported by the Deutsche Forschungsgemeinschaft [ DFG CZ 86/4-1]; the Research Foundation Flanders (FWO), Scientific Research Community [ W000817N]; and the Interuniversity Attraction Poles Programme [ IAPnetwork P7/06], Belgian Science Policy Office. | - |
dc.language.iso | en | - |
dc.rights | © 2017 Elsevier B.V. All rights reserved | - |
dc.subject.other | dependence modeling; multivariate event time data; maximum likelihood estimation; right-censoring; survival analysis; vine copulas | - |
dc.title | Vine copula based inference of multivariate event time data | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 127 | - |
dc.identifier.spage | 109 | - |
dc.identifier.volume | 117 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | [Barthel, Nicole; Killiches, Matthias; Czado, Claudia] Tech Univ Munich, Dept Math, Boltzmannstr 3, D-85748 Garching, Germany. [Geerdens, Candida; Janssen, Paul] Univ Hasselt, BioStat 1, Ctr Stat, Agoralaan 1, B-3590 Diepenbeek, Belgium. | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1016/j.csda.2017.07.010 | - |
dc.identifier.isi | 000414112600008 | - |
dc.identifier.url | https://arxiv.org/pdf/1603.01476.pdf | - |
item.contributor | Barthel, Nicole | - |
item.contributor | GEERDENS, Candida | - |
item.contributor | Killiches, Matthias | - |
item.contributor | JANSSEN, Paul | - |
item.contributor | Czado, Claudia | - |
item.fullcitation | Barthel, Nicole; GEERDENS, Candida; Killiches, Matthias; JANSSEN, Paul & Czado, Claudia (2017) Vine copula based inference of multivariate event time data. In: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 117, p. 109-127. | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2018 | - |
crisitem.journal.issn | 0167-9473 | - |
crisitem.journal.eissn | 1872-7352 | - |
Appears in Collections: | Research publications |
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a.pdf Restricted Access | Published version | 836.77 kB | Adobe PDF | View/Open Request a copy |
VinesSurvival_20170722.pdf | Peer-reviewed author version | 878 kB | Adobe PDF | View/Open |
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