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Title:

Modelling Longitudinal Data using a Pair-Copula Decomposition of Serial Dependence

Document type:
Zeitschriftenaufsatz
Author(s):
Smith, M.; Min, A.; Almeida,C.; Czado,C.
Non-TUM Co-author(s):
ja
Cooperation:
-
Abstract:
Copulas have proven to be very successful tools for the flexible modelling of cross-sectional dependence. In this paper we express the dependence structure of continuous time series data using a sequence of bivariate copulas. This corresponds to a type of decomposition recently called a ‘vine’ in the graphical models literature, where each copula is entitled a ‘pair-copula’. We propose a Bayesian approach for the estimation of this dependence structure for longitudinal data. Bayesian selection i...     »
Intellectual Contribution:
Discipline-based Research
Journal title:
Journal of the American Statistical Association
Year:
2010
Journal volume:
105
Journal issue:
492
Pages contribution:
1467-1479
Reviewed:
ja
Language:
en
Status:
Erstveröffentlichung
Semester:
SS 02
Format:
Text
Judgement review:
0
Key publication:
Ja
Peer reviewed:
Ja
International:
Ja
Book review:
Nein
Commissioned:
not commissioned
Professional Journal:
Nein
Technology:
Nein
Mission statement:
;
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