Methods for Statistical Data Analysis of Multivariate ObservationsWiley, 1977 - 311 стор. A practical guide for multivariate statistical techniques- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of interest. Greatly revised and updated, this Second Edition provides helpful examples, graphical orientation, numerous illustrations, and an appendix detailing statistical software, including the S (or Splus) and SAS systems. It also offers An expanded chapter on cluster analysis that covers advances in pattern recognition New sections on inputs to clustering algorithms and aids for interpreting the results of cluster analysis An exploration of some new techniques of summarization and exposure New graphical methods for assessing the separations among the eigenvalues of a correlation matrix and for comparing sets of eigenvectors Knowledge gained from advances in robust estimation and distributional models that are slightly broader than the multivariate normal This Second Edition is invaluable for graduate students, applied statisticians, engineers, and scientists wishing to use multivariate techniques in a variety of disciplines. |
Зміст
Development and Study of Multivariate | 63 |
Multidimensional Classification and Clustering | 82 |
Statistical Models | 121 |
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A₁ algorithm approach approximate assessing canonical variates centroids chi-squared chi-squared distribution computed configuration contrast vectors coordinates correlation coefficient correlation matrix corresponding covariance matrix CRIMCOORDS data analysis defined denotes departures deviations dimensionality discussion dissimilarity eigenanalysis eigenvalues eigenvectors Euclidean Euclidean distance example gamma distribution Gamma probability plot GAMMA QUANTILES Gnanadesikan graphical groups hierarchical clustering influence function involved Iris setosa joint normality jth response Kettenring largest eigenvalue linear subspace marginal distributions maximum likelihood mean vector measure method multidimensional scaling multiresponse data multivariate nonlinear nonnormality normal distribution null observations obtained order statistics original variables orthogonal outliers p-dimensional points principal components analysis problem procedure Q-Q plot quadratic quantile contour plot QUANTILES regression representation residuals robust estimators sample scatter plot shape parameter Shepard shown in Exhibit smallest solution specific squared distances statistical sum of squares techniques tions transformation Tukey two-dimensional uniresponse univariate unknown values Wilk Y₁ z₁