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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/25163

Title: Vine copula based inference of multivariate event time data
Authors: Barthel, Nicole
Geerdens, Candida
Killiches, Matthias
Janssen, Paul
Czado, Claudia
Issue Date: 2017
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.
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.
URI: http://hdl.handle.net/1942/25163
Link to publication: https://arxiv.org/pdf/1603.01476.pdf
DOI: 10.1016/j.csda.2017.07.010
ISI #: 000414112600008
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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