LEADER 05381nam 2200649 a 450 001 9910876930003321 005 20200520144314.0 010 $a1-281-23734-5 010 $a9786611237349 010 $a0-470-23800-3 010 $a0-470-23799-6 035 $a(CKB)1000000000410752 035 $a(EBL)333727 035 $a(OCoLC)214281350 035 $a(SSID)ssj0000104757 035 $a(PQKBManifestationID)11131038 035 $a(PQKBTitleCode)TC0000104757 035 $a(PQKBWorkID)10086540 035 $a(PQKB)11018215 035 $a(MiAaPQ)EBC333727 035 $a(EXLCZ)991000000000410752 100 $a20070712d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied multiway data analysis /$fPieter M. Kroonenberg 210 $aHoboken, N.J. $cWiley-Interscience$dc2008 215 $a1 online resource (614 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-16497-2 320 $aIncludes bibliographical references (p. 501-526) and indexes. 327 $aAPPLIED MULTIWAY DATA ANALYSIS; CONTENTS; Foreword; Preface; PART I DATA, MODELS, AND ALGORITHMS; 1 Overture; 1.1 Three-way and multiway data; 1.2 Multiway data analysis; 1.3 Before the arrival of three-mode analysis; 1.4 Three-mode data-analytic techniques; 1.5 Example: Judging Chopin's preludes; 1.6 Birth of the Tucker model; 1.7 Current status of multiway analysis; 2 Overview; 2.1 What are multiway data?; 2.2 Why multiway analysis?; 2.3 What is a model?; 2.4 Some history; 2.5 Multiway models and methods; 2.6 Conclusions; 3 Three-Way and Multiway Data; 3.1 Chapter preview; 3.2 Terminology 327 $a3.3 Two-way solutions to three-way data3.4 Classification principles; 3.5 Overview of three-way data designs; 3.6 Fully crossed designs; 3.7 Nested designs; 3.8 Scaling designs; 3.9 Categorical data; 4 Component Models for Fully-Crossed Designs; 4.1 Introduction; 4.2 Chapter preview; 4.3 Two-mode modeling of three-way data; 4.4 Extending two-mode component models to three-mode models; 4.5 Tucker models; 4.6 Parafac models; 4.7 ParaTuck2 model; 4.8 Core arrays; 4.9 Relationships between component models; 4.10 Multiway component modeling under constraints; 4.11 Conclusions 327 $a5 Algorithms for Multiway Models5.1 Introduction; 5.2 Chapter preview; 5.3 Terminology and general issues; 5.4 An example of an iterative algorithm; 5.5 General behavior of multiway algorithms; 5.6 The Parallel factor model - Parafac; 5.7 The Tucker models; 5.8 STATIS; 5.9 Conclusions; PART II DATA HANDLING, MODEL SELECTION, AND INTERPRETATION; 6 Preprocessing; 6.1 Introduction; 6.2 Chapter preview; 6.3 General considerations; 6.4 Model-based arguments for preprocessing choices; 6.5 Content-based arguments for preprocessing choices; 6.6 Preprocessing and specific multiway data designs 327 $a6.7 Centering and analysis-of-variance models: Two-way data6.8 Centering and analysis-of-variance models: Three-way data; 6.9 Recommendations; 7 Missing Data in Multiway Analysis; 7.1 Introduction; 7.2 Chapter preview; 7.3 Handling missing data in two-mode PCA; 7.4 Handling missing data in multiway analysis; 7.5 Multiple imputation in multiway analysis: Data matters; 7.6 Missing data in multiway analysis: Practice; 7.7 Example: Spanjer's Chromatography data; 7.8 Example: NICHD Child care data; 7.9 Further applications; 7.10 Computer programs for multiple imputation 327 $a8 Model and Dimensionality Selection8.1 Introduction; 8.2 Chapter preview; 8.3 Sample size and stochastics; 8.4 Degrees of freedom; 8.5 Selecting the dimensionality of a Tucker model; 8.6 Selecting the dimensionality of a Parafac model; 8.7 Model selection from a hierarchy; 8.8 Model stability and predictive power; 8.9 Example: Chopin prelude data; 8.10 Conclusions; 9 Interpreting Component Models; 9.1 Chapter preview; 9.2 General principles; 9.3 Representations of component models; 9.4 Scaling of components; 9.5 Interpreting core arrays; 9.6 Interpreting extended core arrays 327 $a9.7 Special topics 330 $aFrom a preeminent authority-a modern and applied treatment of multiway data analysis This groundbreaking book is the first of its kind to present methods for analyzing multiway data by applying multiway component techniques. Multiway analysis is a specialized branch of the larger field of multivariate statistics that extends the standard methods for two-way data, such as component analysis, factor analysis, cluster analysis, correspondence analysis, and multidimensional scaling to multiway data. Applied Multiway Data Analysis presents a unique, thorough, and authoritative treatment of 410 0$aWiley series in probability and statistics. 606 $aMultivariate analysis 606 $aMultiple comparisons (Statistics) 606 $aPrincipal components analysis 615 0$aMultivariate analysis. 615 0$aMultiple comparisons (Statistics) 615 0$aPrincipal components analysis. 676 $a519.5/35 700 $aKroonenberg$b Pieter M$0103106 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910876930003321 996 $aApplied multiway data analysis$9103519 997 $aUNINA