An Introduction to Multivariate StatisticsNorth-Holland/New York, 1979 - 350 Seiten Some results on matrices and jacobians of transformations; Multivariate normal distribution; Wishart distributions; Inference on location: hotelling's T2; Linear regression models estimation; Linear models - testing of hypotheses for regression parameters; Inference on covariances; Classification and discrimination; Principal component analysis; Monotonicity and unbiasedness of some power functions. |
Häufige Begriffe und Wortgruppen
a₁ a₂ acceptance region alternative asymptotic distribution asymptotic expansion B₁ B₂ c₁ c₂ ch roots characteristic roots chi square classify consider convex Corollary covariance matrix d.f. and noncentrality D₁ defined denote e₁ equal to 1-a errors of misclassification given by Eq Hence hypothesis H independently distributed invariant test Khatri Lemma likelihood function likelihood-ratio test Math MINQUE moment-generating function monotonicity multivariate normal distributions N₁ N₂ noncentrality parameter nonnull vectors nonsingular matrix normally distributed obtained orthogonal matrix P₁ power function problem of testing PROOF quadratic random variables ratio reject H sample Show simultaneous confidence bounds Srivastava Subsection sufficient statistics symmetric matrix test based testing H testing the hypothesis Theorem transformations unbiased estimator unbiasedness union-intersection test procedure V₁ V₂ variances W₁ Wishart distribution x₁ Y₁ Y₂ zero