The literature dealing with the selection of variables for allocation procedures is examined. Two types of technique are discussed, namely, those which use group separation as the criterion for variable selection and those which more appropriately employ error rates in allocation.

Introduction.- Vector and Matrix Algebra.- The Multivariate Normal Distribution, Multivariate Normality, and Covariance Structure.- One- and Two-Sample Tests.- Multivariate Analysis of Variance.- Discriminant Analysis.- Canonical Correlation.- Principal Component Analysis.- Factor Analysis.- Structural Equations.

Variable selection techniques in discriminant analysis: II. Allocation

Applied Multivariate Analysis

From the Publisher:
A Handbook of Statistical Analyses Using SPSS clearly describes how to conduct a range of univariate and multivariate statistical analyses using the latest version of the Statistical Package for the Social Sciences, SPSS 11. Each chapter addresses a different type of analytical procedure applied to one or more data sets, primarily from the social and behavioral sciences areas. Each chapter also contains exercises relating to the data sets introduced, providing readers with a means to develop both their SPSS and statistical skills. Model answers to the exercises are also provided. Readers can download all of the data sets from a companion Web site furnished by the authors.

/pdf/a-handbook-of-statistical-analyses-using-spss-2n7m9g2irr.pdf

A Handbook of Statistical Analyses Using SPSS

List of Figures List of Tables Preface to Second Edition Acknowledgments Preface to First Edition Notation I INTRODUCTION 1 Discriminant Analysis in Research 11 A Little History 12 Overview 13 Descriptive Discriminant Analysis 14 Predictive Discriminant Analysis 15 Design in Discriminant Analysis 2 Preliminaries 21 Introduction 22 Research Context 23 Data, Analysis Units, Variables, and Constructs 24 Summarizing Data 25 Matrix Operations 26 Distance 27 Linear Composite 28 Probability 29 Statistical Testing 210 Judgment in Data Analysis 211 Summary II ONE-FACTOR MANOVA/DDA 3 Group Separation 31 Introduction 32 Two-Group Analyses 33 Test for Covariance Matrix Equality 34 Yao Test 35 Multiple-Group Analyses-Single Factor 36 Computer Application 37 Summary 4 Assessing MANOVA Effects 41 Introduction 42 Strength of Association 43 Computer Application I 44 Group Contrasts 45 Computer Application II 46 Covariance Matrix Heterogeneity 47 Sample Size 48 Summary 5 Describing MANOVA Effects 51 Introduction 52 Omnibus Effects 53 Computer Application I 54 Standardized LDFWeights 55 LDF Space Dimension 56 Computer Application II 57 Computer Application III 58 Contrast Effects 59 Computer Application IV 510 Summary 6 Deleting and Ordering Variables 61 Introduction 62 Variable Deletion 63 Variable Ordering 64 Contrast Analyses 65 Computer Application II 66 Comments 7 Reporting DDA Results 71 Introduction 72 Example of Reporting DDA Results 73 Computer Package Information 74 Reporting Terms 75 MANOVA/DDA Applications 76 Concerns 77 Overview III FACTORIAL MANOVA, MANCOVA, AND REPEATED MEASURES 8 Factorial MANOVA 81 Introduction 82 Research Context 83 Univariate Analysis 84 Multivariate Analysis 85 Computer Application I 86 Computer Application II 87 Nonorthogonal Design 88 Outcome Variable Ordering and Deletion 89 Summary 9 Analysis of Covariance 91 Introduction 92 Research Context 93 Univariate ANCOVA 94 Multivariate ANCOVA (MANCOVA) 95 Computer Application I 96 Comparing Adjusted Means-Omnibus Test 97 Computer Application II 98 Contrast Analysis 99 Computer Application III 910 Summary 10 Repeated-Measures Analysis 101 Introduction 102 Research Context 103 Univariate Analyses 104 Multivariate Analysis 105 Computer Application I 106 Univariate and Multivariate Analyses 107 Testing for Sphericity 108 Computer Application II 109 Contrast Analysis 1010 Computer Application III 1011 Summary 11 Mixed-Model Analysis 111 Introduction 112 Research Context 113 Univariate Analysis 114 Multivariate Analysis 115 Computer Application I 116 Contrast Analysis 117 Computer Application II 118 Summary IV GROUP MEMBERSHIP PREDICTION 12 Classification Basics 121 Introduction 122 Notion of Distance 123 Distance and Classification 124 Classification Rules in General 125 Comments 13 Multivariate Normal Rules 131 Introduction 132 Normal Density Functions 133 Classification Rules Based on Normality 134 Classification Functions 135 Summary of Classification Statistics 136 Choice of Rule Form 137 Comments 14 Classification Results 141 Introduction 142 Research Context 143 Computer Application 144 Individual Unit Results 145 Group Results 146 Comments 15 Hit Rate Estimation 151 Introduction 152 True Hit Rates 153 Hit Rate Estimators 154 Computer Application 155 Choice of Hit Rate Estimator 156 Outliers and In-Doubt Units 157 Sample Size 158 Comments 16 Effectiveness of Classification Rules 161 Introduction 162 Proportional Chance Criterion 163 Maximum-Chance Criterion 164 Improvement over Chance 165 Comparison of Rules 166 Computer Application I 167 Effect of Unequal Priors 168 PDA Validity/Reliability 169 Applying a Classification Rule to New Units 1610 Comments 17 Deleting and Ordering Predictors 171 Introduction 172 Predictor Deletion 173 Computer Application 174 Predictor Ordering 175 Reanalysis 176 Comments 177 Side Note 18 Two-Group Classification 181 Introduction 182 Two-Group Rule 183 Regression Analogy 184 MRA-PDA Relationship 185 Necessary Sample Size 186 Univariate Classification 19 Nonnormal Rules 191 Introduction 192 Continuous Variables 193 Categorical Variables 194 Predictor Mixtures 195 Comments 20 Reporting PDA Results 201 Introduction 202 Example of Reporting PDA Results 203 Some Additional Specific PDA Information 204 Computer Package Information 205 Reporting Terms 206 Sources of PDA Applications 207 Concerns 208 Overview Further Reading Exercises 21 PDA-Related Analyses 211 Introduction 212 Nonlinear Methods 213 Other Methods V ISSUES AND PROBLEMS 22 Issues in PDA and DDA 221 Introduction 222 Five Choices in PDA 223 Stepwise Analyses 224 StandardizedWeights Versus Structure r's 225 Data-Based Structure 23 Problems in PDA and DDA 231 Introduction 232 Missing Data 233 Outliers and Influential Observations 234 Initial Group Misclassification 235 Misclassification Costs 236 Statistical Versus Clinical Prediction 237 Other Problems Appendix A: Data Set Descriptions Appendix B: Some DA-Related Originators Appendix C: List of Computer Syntax Appendix D: Contents ofWileyWebsite References Answers to Exercises Index

Applied MANOVA and discriminant analysis

Issues in the use and interpretation of discriminant analysis

On propose une nouvelle methode basee sur l'analyse Procrustes et on montre qu'elle fournit un meilleur sous-ensemble pour les donnees analysees en premier

Variable selection techniques in discriminant analysis: II. Allocation

Citations

Applied Multivariate Analysis

A Handbook of Statistical Analyses Using SPSS

Applied MANOVA and discriminant analysis

Issues in the use and interpretation of discriminant analysis

Selection of Variables to Preserve Multivariate Data Structure, Using Principal Components

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