Multivariate Distributions and Normal-Mean Variance Mixtures
Admissible minimax estimators of a mean vector of scale mixtures of multivariate normal distributions
... In this paper, we consider the problem of estimating the mean vector θ of (1.1) with the quadratic loss function kδ−θk 2 . A prior density function considered here is the same one which leads admissibility and ...
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Shape mixtures of multivariate skew-normal distributions
... shape mixtures of two types of multivariate distributions: independent multivariate SN distributions obtained as the product of univariate SN marginal pdf’s from (1) ; and dependent ...
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mixtures of multivariate normal-inverse Gaussian distributions
... In summary, we proposed a fully Bayesian approach for parameter estimation for mixtures of MNIG distributions. We also propose novel approaches to simulate from two distributions: GIG ...
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Characteristic functions of scale mixtures of multivariate skew-normal distributions
... of distributions of many statistics, for example of sums of independent random ...a multivariate distribution is uniquely determined by the distributions of all linear combinations of the component ...
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Normal variance-mean mixtures (I) an inequality between skewness and kurtosis
... The topic of moment inequalities is prominent in Probability and Statistics. For example, such inequalities determine the conditions under which random variables on a given range with given moments exist. For given ...
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Multivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation
... simplicity, mean-variance analysis remains the most widely used asset allocation ...Second, mean-variance frontiers are spanned by only two funds, which simplifies their calculation and ...
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Bayes minimax estimators of the mean of a scale mixture of multivariate normal distributions
... the mean of a variance mixture of multivariate normal distributions is considered under sum of squared errors ...the variance mixture of normal class) which result in ...
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Generalized Bayes minimax estimators of the mean of multivariate normal distribution with unknown variance
... the mean of a multivariate normal the usual assumption is that the variance is known (see [ 3–5 , 9 , 11 , 15 , 17 , 14 , 21 ] are the notable exceptions that cover the case of an unknown ...
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Lecture 3: Continuous distributions, expected value & mean, variance, the normal distribution
... This is incredibly convenient. For example, it is intuitively obvious that the mean of a binomially distributed r.v. Y with parameters n, p is pn. However, it takes some work to show this explicitly by summing ...
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Lecture 8: Linear models and multivariate normal distributions
... where Σ = Cov(X) = AA T is the covariance matrix of X. Note that E[X] = µ by construction. In this case, we will say that X is from a multivariate normal distribution with a mean (vector) µ and a ...
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Good Exact Confidence Sets for a Multivariate Normal Mean
... Ž . this phenomenon is not exceptional. Brown 1966 showed the same inadmis- sibility result for the best invariant estimator of location for a very wide variety of distributions and loss functions. In particular, ...
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On Bayesian inference with conjugate priors for scale mixtures of normal distributions
... the multivariate regression model with distribu- tion of the random responses belonging to the multivariate scale mixtures of normal ...
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The Applications of Mixtures of Normal Distributions in Empirical Finance: A Selected Survey
... the stylized facts or empirical properties of the financial asset returns. From the financial appli- cation perspective, we noted improved results from adopting the MN in the models. However, there are still many ...
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Generalized Normal Mean Variance Mixture and Subordinated Brownian Motion
... generalized normal mean variance distribution and discusses its properties, in partic- ular infinite ...generalized normal mean variance distribution is a subordinated process ...
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Multivariate Logistic Mixtures
... Moreover, we focus on displaying the elevation of the logistic mixture density on the ridgeline and address a technique called the Π-plot, both of which carry important information about modality properties of the ...
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Other Classes of Minimax Estimators of Variance Covariance Matrix in Multivariate Normal Distribution
... where N(M, 7 I p ) denotes that X i are independently distributed as the multivariate normal distribution with the mean + i , the corresponding column of M=(+ 1 , ..., + k ), and the covariance ...
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Extending mixtures of factor models using the restricted multivariate skew-normal distribution
... follow multivariate normal ...whose distributions are moderately or severely skewed. Just like other normal-based mixture models, a slight deviation from normality may seriously affect the ...
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On the Finite Mixture Modeling via Normal Mean-Variance Birnbaum-Saunders Distribution
... scale mixtures of skew-normal distribution models (including: skew-normal (FM-SN), skew-t (FM-ST), skew-contaminated-normal (FM-SCN) and skew-slash ...
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Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions
... following: variance component-based linkage and asso- ciation mapping methods for the estimation of QTL effects; estimating nonadditive genetic effects, such as dominance and epistasis; estimation of genomic ...
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Multivariate gamma distributions *
... Therefore if R is a diagonal matrix with entries 1/v i , (1.1) is the Laplace transform of (ξ α,v 1 , . . . , ξ α,v n ) , in which all the components are independent. For a less trivial example we note that when R is ...
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