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284,436
Generalized SkewElliptical Distributions
, 2002
"... This paper introduces generalized skewelliptical distributions (GSE), which include the multivariate skewnormal, skewt, skewCauchy, and skewelliptical distributions as special cases. GSE are weighted elliptical distributions but the distribution of any even function in GSE random vectors doe ..."
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Cited by 1 (0 self)
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This paper introduces generalized skewelliptical distributions (GSE), which include the multivariate skewnormal, skewt, skewCauchy, and skewelliptical distributions as special cases. GSE are weighted elliptical distributions but the distribution of any even function in GSE random vectors
Multivariate Unified SkewElliptical Distributions
, 2010
"... A class of multivariate unified skewelliptical (SUE) distributions is introduced and studied in detail. In particular, three stochastic representations, the cumulative distribution function, marginal and conditional distributions, linear transformations, additivity, quadratic forms, and moments of ..."
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Cited by 2 (1 self)
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A class of multivariate unified skewelliptical (SUE) distributions is introduced and studied in detail. In particular, three stochastic representations, the cumulative distribution function, marginal and conditional distributions, linear transformations, additivity, quadratic forms, and moments
Generalized SkewElliptical Distributions and their Quadratic Forms
, 2003
"... Abstract This paper introduces generalized skewelliptical distributions (GSE), which include the multivariate skewnormal, skewt, skewCauchy, and skewelliptical distributions as special cases. GSE are weighted elliptical distributions but the distribution of any even function in GSE random vecto ..."
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Abstract This paper introduces generalized skewelliptical distributions (GSE), which include the multivariate skewnormal, skewt, skewCauchy, and skewelliptical distributions as special cases. GSE are weighted elliptical distributions but the distribution of any even function in GSE random
Bayesian Modeling Using a Class of Bimodal SkewElliptical Distributions
, 2008
"... We consider Bayesian inference using an extension of the family of skewelliptical distributions studied by Azzalini (1985, 2005). This new class is referred to as bimodal skewelliptical (BSE) distributions. The elements of the BSE class can take quite different forms. In particular, they can adopt ..."
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We consider Bayesian inference using an extension of the family of skewelliptical distributions studied by Azzalini (1985, 2005). This new class is referred to as bimodal skewelliptical (BSE) distributions. The elements of the BSE class can take quite different forms. In particular, they can
Blind Signal Separation: Statistical Principles
, 2003
"... Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mut ..."
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Cited by 522 (4 self)
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Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption
Distributed Computing in Practice: The Condor Experience
 Concurrency and Computation: Practice and Experience
, 2005
"... Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the worldwide computational grid. In this chapter, we provide the history ..."
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Cited by 542 (7 self)
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Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the worldwide computational grid. In this chapter, we provide
Locally efficient semiparametric estimators for generalized skewelliptical distribution
 J. Am. Statist. Ass
, 2005
"... We consider a class of generalized skewnormal distributions that is useful for selection modeling and robustness analysis and derive a class of semiparametric estimators for the location and scale parameters of the central part of the model. We show that these estimators are consistent and asymptot ..."
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Cited by 11 (8 self)
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function is misspecified, and we compute the loss of efficiency in such cases. We conduct a simulation study and provide an illustrative example. Our method is applicable to generalized skewelliptical distributions.
Mixtures of Probabilistic Principal Component Analysers
, 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
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Cited by 537 (6 self)
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combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Previous attempts to formulate mixture models for PCA have therefore to some extent been ad hoc. In this paper, PCA is formulated within a
Dryad: Distributed DataParallel Programs from Sequential Building Blocks
 In EuroSys
, 2007
"... Dryad is a generalpurpose distributed execution engine for coarsegrain dataparallel applications. A Dryad application combines computational “vertices ” with communication “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of availa ..."
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Cited by 730 (27 self)
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Dryad is a generalpurpose distributed execution engine for coarsegrain dataparallel applications. A Dryad application combines computational “vertices ” with communication “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set
Statistical mechanics of complex networks
 Rev. Mod. Phys
"... Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as ra ..."
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Cited by 2083 (10 self)
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Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real
Results 1  10
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284,436