LEADER 00937nam0-22002891i-450- 001 990003081060403321 035 $a000308106 035 $aFED01000308106 035 $a(Aleph)000308106FED01 035 $a000308106 100 $a20000920d--------km-y0itay50------ba 101 0 $aita 102 $aIT 200 1 $aFollowing the Tracks of Atkinson and Micklewright$ethe Changing Distribution of Income and Earnings in Poland from 1989 to 1995$fby Gian Paolo Caselli, Maurizio Battini 225 1 $aMateriali di discussione$fDipartimento di economia politica. Universitŕ di Modena$v185 676 $aF/1.425 676 $aK/2.0 702 1$aBattini,$bMaurizio 702 1$aCaselli,$bGian Paolo 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003081060403321 952 $aPaper$fSES 959 $aSES 996 $aFollowing the Tracks of Atkinson and Micklewright$9460021 997 $aUNINA DB $aING01 LEADER 05488nam 2200685 a 450 001 9910145562403321 005 20170815120216.0 010 $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$b[electronic resource] /$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. 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