Multivariate Distributions

2019 ◽  
pp. 107-166
Author(s):  
Wolfgang Karl Härdle ◽  
Léopold Simar
Keyword(s):  
Multivariate Distributions

A Note on Multivariate Distributions with Specified Marginals

1988 ◽  
Vol 39 (5) ◽  
pp. 477-479
Author(s):  
Nicholas J. Butterworth
Keyword(s):  
Multivariate Distributions

Multivariate distributions of order κ

1988 ◽  
Vol 7 (3) ◽  
pp. 207-216 ◽  
Author(s):  
Andreas N. Philippou ◽  
Demetris L. Antzoulakos ◽  
Gregory A. Tripsiannis
Keyword(s):  
Multivariate Distributions

Testing multivariate distributions in GARCH models

2008 ◽  
Vol 143 (1) ◽  
pp. 19-36 ◽  
Author(s):  
Jushan Bai ◽  
Zhihong Chen
Keyword(s):  
Garch Models ◽  
Multivariate Distributions

scholarly journals Cumulants of Multivariate Symmetric and Skew Symmetric Distributions

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1383
Author(s):  
Sreenivasa Rao Jammalamadaka ◽  
Emanuele Taufer ◽  
Gyorgy H. Terdik
Keyword(s):  
Comprehensive Treatment ◽  
Multivariate Distributions ◽  
Symmetric Distributions ◽  
Multivariate T Distribution ◽  
T Distribution ◽  
Multivariate T

This paper provides a systematic and comprehensive treatment for obtaining general expressions of any order, for the moments and cumulants of spherically and elliptically symmetric multivariate distributions; results for the case of multivariate t-distribution and related skew-t distribution are discussed in some detail.

scholarly journals Multivariate fractional phase–type distributions

2020 ◽  
Vol 23 (5) ◽  
pp. 1431-1451 ◽  
Author(s):  
Hansjörg Albrecher ◽  
Martin Bladt ◽  
Mogens Bladt
Keyword(s):  
Tail Dependence ◽  
Jump Process ◽  
Multivariate Distributions ◽  
New Class ◽  
Natural Time ◽  
Special Cases ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Markovian Jump Process ◽  
Heavy Tailed

Abstract We extend the Kulkarni class of multivariate phase–type distributions in a natural time–fractional way to construct a new class of multivariate distributions with heavy-tailed Mittag-Leffler(ML)-distributed marginals. The approach relies on assigning rewards to a non–Markovian jump process with ML sojourn times. This new class complements an earlier multivariate ML construction [2] and in contrast to the former also allows for tail dependence. We derive properties and characterizations of this class, and work out some special cases that lead to explicit density representations.

Fluctuations of random walk in R d and storage systems

1977 ◽  
Vol 9 (03) ◽  
pp. 566-587 ◽  
Author(s):  
Priscilla Greenwood ◽  
Moshe Shaked
Keyword(s):  
Random Walk ◽  
Unique Solution ◽  
Convex Cone ◽  
Storage Systems ◽  
Queueing Systems ◽  
Convolution Equation ◽  
Stopping Times ◽  
Multivariate Distributions ◽  
Explicit Formulas ◽  
And Storage

Two Wiener-Hopf type factorization identities for multivariate distributions are introduced. Properties of associated stopping times are derived. The structure that produces one factorization also provides the unique solution of the Wiener-Hopf convolution equation on a convex cone in R d . Some applications for multivariate storage and queueing systems are indicated. For a few cases explicit formulas are obtained for the transforms of the associated stopping times. A result of Kemperman is extended.

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