Multivariate Exponential Distribution

By H. Block. 2006

536 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e2cb7e07cac9555dba1b8d95bead168d498c9e0b

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Copula Theory: An Introduction (2010, background): “’s associated with some shock model had its origin in the seminal paper by Marshall and Olkin (see [133]), where the multivariate exponential distribution was considered (see also [121, 122, 147])).”
Optimal Retention for a Stop-loss Reinsurance Under the VaR and CTE Risk Measures (2007, methodology): “This two-dimensional phase type distribution is also refereed as to a twodimensional Marshall-Olkin distribution (Marshall and Olkin, 1967) or the distribution of the joint-life status in a common shock model (Bowers et al., 1997).”
The Hazard Potential (2006, methodology): “For the purpose of illustration, we limit attention to the bivariate case and consider as seeds the bivariate exponentials of Marshall and Olkin (1967), Gumbel (1960), and Singpurwalla and Youngren (1993; henceforth S–”
Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR (2008, methodology): “The OSCAR prior corresponds to a member of the class of multivariate exponential distributions proposed by Marshall and Olkin (1967). The quadratic programming problem can be large and many standard solvers may have difficulty solving it directly. In the absence of a more efficient solver such as th”
Some positive dependence stochastic orders (2006, unknown): “Let F and G be two n-variate Marshall–Olkin [19] survival functions given, for x 0, by”
Dependence properties and bounds for ruin probabilities in multivariate compound risk models (2007, background): “Let Xj = min{ES : S 3 j}, j = 1, . . . ,m. (4.1) The joint distribution of (X1, . . . , Xm) is called the Marshall-Olkin distribution with parameters {λS, S ⊆ {1, . . . ,m}} (Marshall and Olkin 1967).”
Some Results on the Control of the False Discovery Rate under Dependence (2007, background): “Multivariate exponential random variables, as defined in Marshall & Olkin (1967), are always associated.”
Statistics of Extremes (2004, methodology): “A detailed account of multivariate regular variation can be found in Resnick (1987) and Mikosch (2004); see also Bingham et al.”
Modified Sarhan–Balakrishnan singular bivariate distribution (2010, methodology): “The SBV distribution has both an absolute continuous part and a singular part similar to the bivariate exponential model of Marshall and Olkin (1967), the bivariate models proposed by Sarhan and Balakrishnan (2007) and the bivariate generalized exponentialmodel of Kundu andGupta (2009).”
Asymptotic Analysis of Multivariate Tail Conditional Expectations (2012, methodology): “Let R have the survival function F (t) = (1 + t) −α for t ≥ 0 and α > 1, and let (T1, T2) have a bivariate Marshall-Olkin exponential distribution function on [0,∞)(2) [19], namely, T1 = min{E1, E12}, T2 = min{E2, E12}, where E1, E2, E12 are independent and have the exponential distributions with pa”
Bivariate inverse Weibull distribution (2016, background): “The main aim of this paper is to provide a bivariate IW distribution whose marginals have IW distributions using an idea similar to that of Theorem 3.2 proposed by Marshall and Olkin.”
A Comparative Analysis of CDO Pricing Models under the Factor Copula Framework (2009, methodology): “” They are also known as multivariate exponential models in Marshall and Olkin [1967]. They were introduced to the credit domain by Duffie and Singleton [1998] and also discussed by Li [2000] and Wong [2000]. More ρ θ θ K = +2”
Numerical solution of two asset jump diffusion models for option valuation (2008, background): “The analysis shows that the fixed point iteration is rapidly convergent under typical market parameters.”