The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Laure Dumaz - One of the best experts on this subject based on the ideXlab platform.
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The right tail exponent of the Tracy–Widom $\Beta$ Distribution
Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, 2013Co-Authors: Laure Dumaz, Bálint VirágAbstract:The Tracy-Widom Beta Distribution is the large dimensional limit of the top eigenvalue of Beta random matrix ensembles. We use the stochastic Airy operator representation to show that as a tends to infinity the tail of the Tracy Widom Distribution satisfies P(TW_Beta > a) = a^(-3/4 Beta+o(1)) exp(-2/3 Beta a^(3/2)).
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the right tail exponent of the tracy widom Beta Distribution
Annales De L Institut Henri Poincare-probabilites Et Statistiques, 2013Co-Authors: Laure Dumaz, Bálint VirágAbstract:The Tracy–Widom $\Beta$ Distribution is the large dimensional limit of the top eigenvalue of $\Beta$ random matrix ensembles. We use the stochastic Airy operator representation to show that as $a\to\infty$ the tail of the Tracy–Widom Distribution satisfies \[P(\mathit{TW}_{\Beta}>a)=a^{-(3/4)\Beta+\mathrm{o}(1)}\exp\biggl(-\frac{2}{3}\Beta a^{3/2}\biggr).\]
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the right tail exponent of the tracy widom Beta Distribution
arXiv: Probability, 2011Co-Authors: Laure Dumaz, Bálint VirágAbstract:The Tracy-Widom Beta Distribution is the large dimensional limit of the top eigenvalue of Beta random matrix ensembles. We use the stochastic Airy operator representation to show that as a tends to infinity the tail of the Tracy Widom Distribution satisfies P(TW_Beta > a) = a^(-3/4 Beta+o(1)) exp(-2/3 Beta a^(3/2)).
Bálint Virág - One of the best experts on this subject based on the ideXlab platform.
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The right tail exponent of the Tracy–Widom $\Beta$ Distribution
Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, 2013Co-Authors: Laure Dumaz, Bálint VirágAbstract:The Tracy-Widom Beta Distribution is the large dimensional limit of the top eigenvalue of Beta random matrix ensembles. We use the stochastic Airy operator representation to show that as a tends to infinity the tail of the Tracy Widom Distribution satisfies P(TW_Beta > a) = a^(-3/4 Beta+o(1)) exp(-2/3 Beta a^(3/2)).
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the right tail exponent of the tracy widom Beta Distribution
Annales De L Institut Henri Poincare-probabilites Et Statistiques, 2013Co-Authors: Laure Dumaz, Bálint VirágAbstract:The Tracy–Widom $\Beta$ Distribution is the large dimensional limit of the top eigenvalue of $\Beta$ random matrix ensembles. We use the stochastic Airy operator representation to show that as $a\to\infty$ the tail of the Tracy–Widom Distribution satisfies \[P(\mathit{TW}_{\Beta}>a)=a^{-(3/4)\Beta+\mathrm{o}(1)}\exp\biggl(-\frac{2}{3}\Beta a^{3/2}\biggr).\]
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the right tail exponent of the tracy widom Beta Distribution
arXiv: Probability, 2011Co-Authors: Laure Dumaz, Bálint VirágAbstract:The Tracy-Widom Beta Distribution is the large dimensional limit of the top eigenvalue of Beta random matrix ensembles. We use the stochastic Airy operator representation to show that as a tends to infinity the tail of the Tracy Widom Distribution satisfies P(TW_Beta > a) = a^(-3/4 Beta+o(1)) exp(-2/3 Beta a^(3/2)).
Saralees Nadarajah - One of the best experts on this subject based on the ideXlab platform.
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a new bivariate Beta Distribution
Statistics, 2017Co-Authors: Saralees Nadarajah, Shou Hsing Shih, Daya K NagarAbstract:ABSTRACTA new bivariate Beta Distribution capable of providing better fits than all its competitors is introduced. Various representations are derived for its product moments, marginal densities, marginal moments, conditional densities and conditional moments. The method of maximum likelihood is used to derive the associated estimation procedure. Applications to six bivariate data sets are illustrated.
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The bivariate Gauss hypergeometric Beta Distribution
Integral Transforms and Special Functions, 2008Co-Authors: Saralees NadarajahAbstract:A new bivariate Beta Distribution based on the Gauss hypergeometric function is introduced. Various representations are derived for its product moments, marginal densities, marginal moments, conditional densities and conditional moments. The method of maximum likelihood is used to derive the associated estimation procedure as well as the Fisher information matrix.
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A truncated inverted Beta Distribution with application to air pollution data
Stochastic Environmental Research and Risk Assessment, 2007Co-Authors: Saralees NadarajahAbstract:The inverted Beta Distributions are popular models for hydrology. However, they suffer from the fact that they do not possess finite moments of all orders. In this note, a truncated version of the inverted Beta Distribution is introduced, which possesses finite moments of all orders and could therefore be a better model for hydrological data with a finite upper bound. Explicit expressions for the moments of the truncated Distribution and its estimation procedure are derived. An application is provided to ozone level data from New York.
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The Bivariate F2–Beta Distribution
American Journal of Mathematical and Management Sciences, 2007Co-Authors: Saralees NadarajahAbstract:SYNOPTIC ABSTRACTA new bivariate Beta Distribution based on the Appell function of the second kind is introduced. Various representations are derived for its product moments, marginal densities, marginal moments, conditional densities and conditional moments. The method of maximum likelihood is used to derive the associated estimation procedure as well as the Fisher information matrix. Finally, an application to drought data from Nebraska is illustrated.
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A compound Beta Distribution with applications in finance
Statistical Methods and Applications, 2006Co-Authors: Saralees Nadarajah, Arjun K. GuptaAbstract:If X and Y are gamma distributed independent random variables then it is well known that the ratio X / (X + Y) has the Beta Distribution. In this note, the Distribution of W = X / (X + Y) is considered when X and Y have the compound gamma Distribution. We refer to the Distribution of W as compound Beta and describe an application to consumer price indices to show that compound Beta is a better model than one based on the standard Beta Distribution. We derive various properties of W, including its probability density function, cumulative Distribution function, hazard rate function and moments.
Robert W. Johnson - One of the best experts on this subject based on the ideXlab platform.
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Transformation Group Approach To Applications Of The Beta Distribution
arXiv: Data Analysis Statistics and Probability, 2013Co-Authors: Robert W. JohnsonAbstract:A transformation group approach to the prior for the parameters of the Beta Distribution is suggested. The relationship between the Beta Distribution and the Poisson and gamma Distributions in the continuum is derived. Comparison to the maximum likelihood estimate of the parameters from an empirical Distribution of network links is performed. The effect of the prior on the analysis of some well known examples from statistical genetics also is examined. Use of the Beta Distribution in the classification problem is discussed.
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Applications of the Beta Distribution Part 1: Transformation Group Approach
arXiv: Data Analysis Statistics and Probability, 2013Co-Authors: Robert W. JohnsonAbstract:A transformation group approach to the prior for the parameters of the Beta Distribution is suggested which accounts for finite sets of data by imposing a limit to the range of parameter values under consideration. The relationship between the Beta Distribution and the Poisson and gamma Distributions in the continuum is explored, with an emphasis on the decomposition of the model into separate estimates for size and shape. Use of the Beta Distribution in classification and prediction problems is discussed, and the effect of the prior on the analysis of some well known examples from statistical genetics is examined.
Ramon Gutierrez Jaimez - One of the best experts on this subject based on the ideXlab platform.
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singular matrix variate Beta Distribution
Journal of Multivariate Analysis, 2008Co-Authors: Jose A Diazgarcia, Ramon Gutierrez JaimezAbstract:In this paper, we determine the symmetrised density of doubly noncentral singular matrix variate Beta type I and II Distributions under different definitions. As particular cases we obtain the noncentral singular matrix variate Beta type I and II Distributions and the corresponding joint density of the nonnull eigenvalues. In addition, we propose an alternative approach to find the corresponding nonsymmetrised densities. From the latter, we solve the integral proposed by Constantine [Noncentral Distribution problems in multivariate analysis, Ann. Math. Statist. 34 (1963) 1270-1285] and Khatri [A note on Mitra's paper ''A density free approach to the matrix variate Beta Distribution'', Sankhya A 32 (1970) 311-318] and reconsidered in Farrell [Multivariate Calculation: Use of the Continuous Groups, Springer Series in Statistics, Springer, New York, 1985, p. 191], see also Diaz-Garcia and Gutierrez-Jaimez [Noncentral matrix variate Beta Distribution, Comunicacion Tecnica, No. I-06-06 (PE/CIMAT), Guanajuato, Mexico, 2006, ], for the singular and nonsingular cases.
