Applied Multivariate Analysis: Using Bayesian and Frequentist Methods of Inference
Matrix theory useful in multivariate analysis; Continuous multivariate distributions. The normal distribution, Bayesian inference; Multivariate large sample distributions and approximations; The wishart and related distributions; Other continuous multivariate distributions; Basic multivariate statitics in the normal distribution; Regression and the analysis of variance; Principal components; Factor analysis and latent structure analysis; Canonical correlation; Stable portfolio analysis; Classification and discrimination models; Control in the multivariate linear model; Structuring multivariate populations (multidimensional scaling and clustering.
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Matrix Theory Useful in Multivariate Analysis
Continuous Multivariate Distributions
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analysis of variance applied assumed assumptions asymptotic Bayesian canonical correlations Chapter characteristic function classify clustering Code Number column conjugate prior constant correlation matrix covariance matrix defined denote dependent variable Description and Remarks Developer of Program diag diagonal diffuse prior Dirichlet distribution elements equation errors Example factor analysis Form of Output given Hence hypothesis independent inferences large samples latent class latent roots latent vectors likelihood function likelihood ratio linear Machine Language MANOVA maximum likelihood estimators multivariate analysis multivariate Normal multivariate Normal distribution Name of Program natural conjugate prior Normal distribution Number of Program observations obtained orthogonal p-vector parameters plim populations posterior distribution principal components prior density prior distribution problem procedure Program and Affiliation Program Was Written Proof random variables result scalar Section solution stable distributions statistic Suppose Theorem tion univariate values variate Wishart distribution zero zonal polynomials