Description
Multivariate Statistical Simulation Mark E. Johnson For the researcher in statistics, probability, and operations research involved in the design and execution of a computer-aided simulation study utilizing continuous multivariate distributions, this book considers the properties of such distributions from a unique perspective. With enhancing graphics (three-dimensional and contour plots), it presents generation algorithms revealing features of the distribution undisclosed in preliminary algebraic manipulations. Well-known multivariate distributions covered include normal mixtures, elliptically assymmetric, Johnson translation, Khintine, and Burr-Pareto-logistic. 1987 (0 471-82290-6) 230 pp. Aspects of Multivariate Statistical Theory Robb J. Muirhead A classical mathematical treatment of the techniques, distributions, and inferences based on the multivariate normal distributions. The main focus is on distribution theory - both exact and asymptotic.
Introduces three main areas of current activity overlooked or inadequately covered in existing texts: noncentral distribution theory, decision theoretic estimation of the parameters of a multivariate normal distribution, and the uses of spherical and elliptical distributions in multivariate analysis. 1982 (0 471-09442-0) 673 pp. Multivariate Observations G. A. F. Seber This up-to-date, comprehensive sourcebook treats data-oriented techniques and classical methods. It concerns the external analysis of differences among objects, and the internal analysis of how the variables measured relate to one another within objects. The scope ranges from the practical problems of graphically representing high dimensional data to the theoretical problems relating to matrices of random variables. 1984 (0 471-88104-X) 686 pp.
Table of Contents
The Multivariate Normal Distribution. Estimation of the Mean Vector and the Covariance Matrix. The Distributions and Uses of Sample Correlation Coefficients. The Generalized T2-Statistic. Classification of Observations. The Distribution of the Sample Covariance Matrix and the Sample Generalized Variance. Testing the General Linear Hypothesis: Multivariate Analysis of Variance. Testing Independence of Sets of Variates. Testing Hypotheses of Equality of Covariance Matrices and Equality of Mean Vectors and Covariance Matrices. Principal Components. Canonical Correlations and Canonical Variables. The Distributions of Characteristic Roots and Vectors. Factor Analysis. Appendixes. References. Index.
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