Applied multivariate analysis
Matrix theory useful in multivariate analysis; Continuous multivariate distributions: the normal distribution; Multivariate large sample distributions and approximations; The Wishart and related distributions; Other continuous multivariate distributions; Basic multivariate statistics in the normal distribution; Regression and the analysis of variance; Principal components; Factor analysis and latent structure analysis; Canonical correlations; Stable portfolio analysis; Classification and discrimination models; Control in the multivariate linear model; Structuring multivariate populations (Multidimensional scaling and clustering).
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Multivariate Large Sample Distributions
Other Continuous Multivariate Distributions
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applied assumed assumptions asymptotic canonical correlations Chapter characteristic function classified clustering Code Number column conjugate prior correlation matrix covariance matrix defined denote dependent variable Description and Remarks Developer of Program diag diagonal diffuse prior Dirichlet distribution elements equation Example factor analysis Form of Output Fortran II Limitations given Hence hypothesis independent inferences large samples latent class latent class model latent roots latent vectors least squares estimator 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 sampling theory scalar Section solution stable distributions statistic Suppose Theorem tion variate Wishart distribution zero