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Multivariate Normal Distribution
"... • We will almost always assume that the joint distribution of the p × 1 vectors of measurements on each sample unit is the pdimensional multivariate normal distribution. • The MVN assumption is often appropriate: – Variables can sometimes be assumed to be multivariate normal (perhaps after transfor ..."
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• We will almost always assume that the joint distribution of the p × 1 vectors of measurements on each sample unit is the pdimensional multivariate normal distribution. • The MVN assumption is often appropriate: – Variables can sometimes be assumed to be multivariate normal (perhaps after
Clustering Multivariate Normal Distributions
"... Abstract. In this paper, we consider the task of clustering multivariate normal distributions with respect to the relative entropy into a prescribed number, k, of clusters using a generalization of Lloyd’s kmeans algorithm [1]. We revisit this informationtheoretic clustering problem under the ausp ..."
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Cited by 3 (1 self)
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Abstract. In this paper, we consider the task of clustering multivariate normal distributions with respect to the relative entropy into a prescribed number, k, of clusters using a generalization of Lloyd’s kmeans algorithm [1]. We revisit this informationtheoretic clustering problem under
FOR THE MULTIVARIATE NORMAL DISTRIBUTION II
, 1969
"... In a recent paper of the same title, John (1968) considered the problem of determining from a random sample of size N from a pvariate normal distribution a region which with probability S ..."
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In a recent paper of the same title, John (1968) considered the problem of determining from a random sample of size N from a pvariate normal distribution a region which with probability S
An AcceptReject Algorithm For the Positive Multivariate Normal Distribution
, 2010
"... The need to simulate from a positive multivariate normal distribution arises in several settings, specifically in Bayesian analysis. A variety of algorithms can be used to sample from this distribution, but most of these algorithms involve Gibbs sampling. Since the sample is generated from a Markov ..."
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The need to simulate from a positive multivariate normal distribution arises in several settings, specifically in Bayesian analysis. A variety of algorithms can be used to sample from this distribution, but most of these algorithms involve Gibbs sampling. Since the sample is generated from a Markov
VALUES FOR THE CUMULATIVE DISTRIBUTION FUNCTION OF THE STANDARD MULTIVARIATE NORMAL DISTRIBUTION
"... This paper provides cumulative distribution function values for the standard multivariate normal distribution. The values are derived from a simulation model for the multivariate normal distribution, which can be run on any standard personal computer. Utilizing userinput distribution parameters, th ..."
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This paper provides cumulative distribution function values for the standard multivariate normal distribution. The values are derived from a simulation model for the multivariate normal distribution, which can be run on any standard personal computer. Utilizing userinput distribution parameters
Multivariate normal distribution Wikipedia, the free encyclopedia 9/18/08 9:04 AM Multivariate normal distribution
"... From Wikipedia, the free encyclopedia Make a donation to Wikipedia and give the gift of knowledge! MVN redirects here. For the airport with that IATA code in Mount Vernon, Illinois, see Mount Vernon Airport. In probability theory and statistics, a multivariate normal distribution, sometimes also cal ..."
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From Wikipedia, the free encyclopedia Make a donation to Wikipedia and give the gift of knowledge! MVN redirects here. For the airport with that IATA code in Mount Vernon, Illinois, see Mount Vernon Airport. In probability theory and statistics, a multivariate normal distribution, sometimes also
Efficient Computation Of Statistics For Banded Multivariate Normal Distributions
"... We look at the problem of computing statistics for a large multivariate normal distribution whose precision matrix has limited bandwidth. In particular, we wish to randomly sample from the distribution, compute its mean vector, and compute a central band of its covariance matrix. We show how to acco ..."
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We look at the problem of computing statistics for a large multivariate normal distribution whose precision matrix has limited bandwidth. In particular, we wish to randomly sample from the distribution, compute its mean vector, and compute a central band of its covariance matrix. We show how
A CHARACTERIZATION OF THE MULTIVARIATE NORMAL DISTRIBUTION BY USING THE HAZARD GRADIENT
, 2002
"... Abstract. We give a general result to characterize a multivariate distribution from a relationship between the left truncated mean function and the hazard gradient function. This result allows us to obtain new characterizations of multivariate distributions. In particular, we show that, for the mul ..."
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, for the multivariate normal distribution, the simple relationship, obtained in standardized form by McGill (1992, Communications in Statistics. Theory Methods, 21(11), 30533060), actually characterizes the multivariate normal distribution. Key words and phrases: Hazard gradient function, failure rate, mean residual
Numerical Evaluation of Singular Multivariate Normal Distributions
 Journal of Statistical Computation and Simulation
, 1999
"... We present an efficient and accurate method to evaluate multivariate normal probabilities with arbitrary singular correlation matrices. The new method is applied to the construction of simultaneous confidence intervals and simultaneous all pairwise confidence intervals for multinomial proportions wh ..."
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Cited by 11 (1 self)
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when the sample size is sufficiently large. Key Words: multivariate normal, singular distribution, numerical integration, statistical computation. Submitted to Computational Statistics and Data Analysis 1 1 Introduction Let X 1 ; : : : ; Xm (m 2) be the standardized mvariate normal random
Results 1  10
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