LEADER 04304nam 22006255 450 001 9910822030403321 005 20240516013446.0 010 $a1-4757-2765-8 024 7 $a10.1007/978-1-4757-2765-4 035 $a(CKB)2660000000022218 035 $a(SSID)ssj0001296693 035 $a(PQKBManifestationID)11709035 035 $a(PQKBTitleCode)TC0001296693 035 $a(PQKBWorkID)11353021 035 $a(PQKB)10543650 035 $a(DE-He213)978-1-4757-2765-4 035 $a(MiAaPQ)EBC3085017 035 $a(PPN)238028356 035 $a(EXLCZ)992660000000022218 100 $a20130220d1997 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 12$aA First Course in Multivariate Statistics /$fby Bernard Flury 205 $a1st ed. 1997. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d1997. 215 $a1 online resource (XV, 715 p. 20 illus.) 225 1 $aSpringer Texts in Statistics,$x1431-875X 300 $a"With 141 Figures." 311 $a0-387-98206-X 311 $a1-4419-3113-9 320 $aIncludes bibliographical references and index. 327 $a1. Why Multivariate Statistics? -- 2. Joint Distribution of Several Random Variables -- 3. The Multivariate Normal Distribution -- 4. Parameter Estimation -- 5. Discrimination and Classification, Round 1 -- 6. Statistical Inference for Means -- 7. Discrimination and Classification, Round 2 -- 8. Linear Principal Component Analysis -- 9. Normal Mixtures -- Appendix: Selected Results From Matrix Algebra -- A.0. Preliminaries -- A.1. Partitioned Matrices -- A.2. Positive Definite Matrices -- A.3. The Cholesky Decomposition -- A.4. Vector and Matrix Differentiation -- A.5. Eigenvectors and Eigenvalues -- A.6. Spectral Decomposition of Symmetric Matrices -- A.7. The Square Root of a Positive Definite Symmetric Matrix -- A.8. Orthogonal Projections on Lines and Planes -- A.9. Simultaneous Decomposition of Two Symmetric Matrices. 330 $aMy goal in writing this book has been to provide teachers and students of multi­ variate statistics with a unified treatment ofboth theoretical and practical aspects of this fascinating area. The text is designed for a broad readership, including advanced undergraduate students and graduate students in statistics, graduate students in bi­ ology, anthropology, life sciences, and other areas, and postgraduate students. The style of this book reflects my beliefthat the common distinction between multivariate statistical theory and multivariate methods is artificial and should be abandoned. I hope that readers who are mostly interested in practical applications will find the theory accessible and interesting. Similarly I hope to show to more mathematically interested students that multivariate statistical modelling is much more than applying formulas to data sets. The text covers mostly parametric models, but gives brief introductions to computer-intensive methods such as the bootstrap and randomization tests as well. The selection of material reflects my own preferences and views. My principle in writing this text has been to restrict the presentation to relatively few topics, but cover these in detail. This should allow the student to study an area deeply enough to feel comfortable with it, and to start reading more advanced books or articles on the same topic. 410 0$aSpringer Texts in Statistics,$x1431-875X 606 $aProbabilities 606 $aStatistics  606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 615 0$aProbabilities. 615 0$aStatistics . 615 14$aProbability Theory and Stochastic Processes. 615 24$aStatistical Theory and Methods. 676 $a519.2 676 $a519.535 700 $aFlury$b Bernhard$f1951-$4aut$4http://id.loc.gov/vocabulary/relators/aut$0102068 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910822030403321 996 $aFirst course in multivariate statistics$9415831 997 $aUNINA