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Title:

Multivariate geometric distributions, (logarithmically) monotone sequences, and infinitely divisible laws

Document type:
Zeitschriftenaufsatz
Author(s):
Mai, J.-F.; Scherer, M.; Shenkman, N.
Non-TUM Co-author(s):
nein
Cooperation:
-
Abstract:
Two stochastic representations of multivariate geometric distributions are analyzed, both are obtained by lifting the lack-of-memory (LM) property of the univariate geometric law to the multivariate case. On the one hand, the narrow-sense multivariate geometric law can be considered a discrete equivalent of the well-studied Marshall-Olkin exponential law. On the other hand, the more general wide-sense geometric law is shown to be characterized by the LM property and can differ significantly from...     »
Keywords:
multivariate geometric law, lack-of-memory, exchangeability, completely monotone sequences, d-monotone sequences, de Finetti's theorem, conditionally i.i.d., infinitely divisible law
Intellectual Contribution:
Discipline-based Research
Journal title:
Journal of Multivariate Analysis
Year:
2013
Journal volume:
115
Pages contribution:
457–480
Reviewed:
ja
Language:
en
Status:
Verlagsversion / published
TUM Institution:
Lehrstuhl für Finanzmathematik
Format:
Text
Key publication:
Nein
Peer reviewed:
Ja
International:
Ja
Book review:
Nein
Commissioned:
not commissioned
Professional Journal:
Nein
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