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The Multivariate Heteroscedastic Method: Distributions of Statistics and an Application
American Journal of Mathematical and Management Sciences, 1987SYNOPTIC ABSTRACTAsymptotic approximations to the distributions of the basic variate, and of some statistics constructed by the heteroscedastic method are derived for the multivariate case. The results are numerically compared with exact ones in the case of one and/or two dimensions. As an application, simultaneous confidence interval estimation with a
Hiroto Hyakutake, Minoru Siotani
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MCQMC Methods for Multivariate Statistical Distributions
2008A review and comparison is presented for the use of Monte Carlo and Quasi-Monte Carlo methods for multivariate Normal and multivariate t distribution computation problems. Spherical-radial transformations, and separation-of-variables transformations for these problems are considered. The use of various Monte Carlo methods, Quasi-Monte Carlo methods and
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Projection of circuit performance distributions by multivariate statistics
IEEE Transactions on Semiconductor Manufacturing, 1989Production test data from process monitoring test structures were utilized in a circuit simulator that accounts for the correlations between circuit elements. This 'correlated' simulation is based on a principal component analysis technique that requires the means, the standard deviations, and the correlation coefficients of all circuit elements.
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Order Statistics of Samples from Multivariate Distributions
Journal of the American Statistical Association, 1975Abstract Let (X 1j , X 2j , ···, Xmj ), j = 1, 2, ···, n, be a sample of size n on an m-dimensional vector (X 1, X 2, ···, Xm ), m ≥ 2. Let the order statistics of the rth component be denoted by X r,1* ≤ X r,2* ≤ ··· ≤ X r,n *. In this article we investigate the distribution of the vector (X 1,n−i1*, X 2,n–i2*, ···, Xm,n–im *) for (i 1, i 2, ···, im )
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A Kotz-Type Distribution for Multivariate Statistical Inference
2007In this chapter, we consider a Kotz-type distribution (of a p-variate random vector X) which has fatter tail regions than that of multivariate normal distribution, and its probability density function (pdf) is given by $$ f(x,\mu ,\Sigma ) = c_p \left| \Sigma \right|^{ - \tfrac{1} {2}} \exp \{ - [(x - \mu )'\Sigma ^{ - 1} (x - \mu )]^{\tfrac{1} {2}}
Dayanand N. Naik, Kusaya Plungpongpun
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Distributions in Statistics: Continuous Multivariate Distributions.
Biometrics, 1971John E. Rowcroft, N. L. Johnson, S. Kotz
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Distributions in Statistics: Continuous Multivariate Distributions
Technometrics, 1974openaire +1 more source
On the distribution of a complex multivariate statistic
Archiv der Mathematik, 1971openaire +2 more sources
Statistical Computing Related to the Multivariate Normal Distribution
1990In this chapter we discuss some useful methods concerning statistical computing related to the multivariate normal distribution. Section 8.1 deals with methods for generating random variates from a multivariate normal distribution in simulation studies. The methods involve linear transformations of i.i.d.
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