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 Izenman, Alan Julian.
 New York ; London : Springer, c2008.
 Description
 Book — xxv, 731 p. : ill.
 Izenman, Alan Julian.
 New York ; [London] : Springer, c2008.
 Description
 Book — xxv, 731 p. : ill. ; 24 cm.
 Summary

 Preface.  Introduction and preview.  Data and databases.  Random vectors and matrices.  Nonparametric density estimation.  Multiple regression and model assessment.  Multivariate regression.  Linear dimensionality reduction.  Linear discriminant analysis.  Recursive partitioning and decision trees.  Artificial nueral networks.  Support vector machines.  Cluster analysis.  Multidimensional scaling and distance geometry.  Committee machines.  Nonlinear dimensionality reduction.  Wavelets.  Correspondence analysis.  Notation and mathematical results.  References.
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 Johnson, Richard A. (Richard Arnold), 1937 author.
 Sixth edition.  [New York, NY] : Pearson Education, Inc., [2019]
 Description
 Book — xviii, 773 pages ; 24 cm.
 Summary

For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduatelevel courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this marketleading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.
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QA278 .J63 2019  Unknown 
 Mertler, Craig A., author.
 Sixth edition.  New York : Routledge, Taylor & Francis Group, 2017.
 Description
 Book — xvi, 374 pages : illustrations ; 29 cm
 Summary

 Introduction to multivariate statistics
 A guide to multivariate techniques
 Preanalysis data screening
 Factorial analysis of variance
 Analysis of covariance
 Multivariate analysis of variance and covariance
 Multiple regression
 Path analysis
 Factor analysis
 Discriminant analysis
 Logistic regression.
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QA278 .M47 2017  Unknown 
5. Categorical data analysis [2013]
 Agresti, Alan.
 3rd ed.  Hoboken, NJ : Wiley, c2013.
 Description
 Book — xvi, 714 p. : ill. ; 27 cm.
 Summary

 Preface xiii
 1 Introduction: Distributions and Inference for Categorical Data
 1
 1.1 Categorical Response Data,
 1
 1.2 Distributions for Categorical Data,
 5
 1.3 Statistical Inference for Categorical Data,
 8
 1.4 Statistical Inference for Binomial Parameters,
 13
 1.5 Statistical Inference for Multinomial Parameters,
 17
 1.6 Bayesian Inference for Binomial and Multinomial Parameters,
 22
 Notes,
 27
 Exercises,
 28
 2 Describing Contingency Tables
 37
 2.1 Probability Structure for Contingency Tables,
 37
 2.2 Comparing Two Proportions,
 43
 2.3 Conditional Association in Stratified
 2 x
 2 Tables,
 47
 2.4 Measuring Association in I x J Tables,
 54
 Notes,
 60
 Exercises,
 60
 3 Inference for TwoWay Contingency Tables
 69
 3.1 Confidence Intervals for Association Parameters,
 69
 3.2 Testing Independence in Twoway Contingency Tables,
 75
 3.3 Followingup ChiSquared Tests,
 80
 3.4 TwoWay Tables with Ordered Classifications,
 86
 3.5 SmallSample Inference for Contingency Tables,
 90
 3.6 Bayesian Inference for Twoway Contingency Tables,
 96
 3.7 Extensions for Multiway Tables and Nontabulated Responses,
 100
 Notes,
 101
 Exercises,
 103
 4 Introduction to Generalized Linear Models
 113
 4.1 The Generalized Linear Model,
 113
 4.2 Generalized Linear Models for Binary Data,
 117
 4.3 Generalized Linear Models for Counts and Rates,
 122
 4.4 Moments and Likelihood for Generalized Linear Models,
 130
 4.5 Inference and Model Checking for Generalized Linear Models,
 136
 4.6 Fitting Generalized Linear Models,
 143
 4.7 QuasiLikelihood and Generalized Linear Models,
 149
 Notes,
 152
 Exercises,
 153
 5 Logistic Regression
 163
 5.1 Interpreting Parameters in Logistic Regression,
 163
 5.2 Inference for Logistic Regression,
 169
 5.3 Logistic Models with Categorical Predictors,
 175
 5.4 Multiple Logistic Regression,
 182
 5.5 Fitting Logistic Regression Models,
 192
 Notes,
 195
 Exercises,
 196
 6 Building, Checking, and Applying Logistic Regression Models
 207
 6.1 Strategies in Model Selection,
 207
 6.2 Logistic Regression Diagnostics,
 215
 6.3 Summarizing the Predictive Power of a Model,
 221
 6.4 Mantel Haenszel and Related Methods for Multiple
 2 x
 2 Tables,
 225
 6.5 Detecting and Dealing with Infinite Estimates,
 233
 6.6 Sample Size and Power Considerations,
 237
 Notes,
 241
 Exercises,
 243
 7 Alternative Modeling of Binary Response Data
 251
 7.1 Probit and Complementary Log log Models,
 251
 7.2 Bayesian Inference for Binary Regression,
 257
 7.3 Conditional Logistic Regression,
 265
 7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models,
 270
 7.5 Issues in Analyzing HighDimensional Categorical Data,
 278
 Notes,
 285
 Exercises,
 287
 8 Models for Multinomial Responses
 293
 8.1 Nominal Responses: BaselineCategory Logit Models,
 293
 8.2 Ordinal Responses: Cumulative Logit Models,
 301
 8.3 Ordinal Responses: Alternative Models,
 308
 8.4 Testing Conditional Independence in I x J x K Tables,
 314
 8.5 DiscreteChoice Models,
 320
 8.6 Bayesian Modeling of Multinomial Responses,
 323
 Notes,
 326
 Exercises,
 329
 9 Loglinear Models for Contingency Tables
 339
 9.1 Loglinear Models for Twoway Tables,
 339
 9.2 Loglinear Models for Independence and Interaction in Threeway Tables,
 342
 9.3 Inference for Loglinear Models,
 348
 9.4 Loglinear Models for Higher Dimensions,
 350
 9.5 Loglinear Logistic Model Connection,
 353
 9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions,
 356
 9.7 Loglinear Model Fitting: Iterative Methods and Their Application,
 364
 Notes,
 368
 Exercises,
 369
 10 Building and Extending Loglinear Models
 377
 10.1 Conditional Independence Graphs and Collapsibility,
 377
 10.2 Model Selection and Comparison,
 380
 10.3 Residuals for Detecting CellSpecific Lack of Fit,
 385
 10.4 Modeling Ordinal Associations,
 386
 10.5 Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis,
 393
 10.6 Empty Cells and Sparseness in Modeling Contingency Tables,
 398
 10.7 Bayesian Loglinear Modeling,
 401
 Notes,
 404
 Exercises,
 407
 11 Models for Matched Pairs
 413
 11.1 Comparing Dependent Proportions,
 414
 11.2 Conditional Logistic Regression for Binary Matched Pairs,
 418
 11.3 Marginal Models for Square Contingency Tables,
 424
 11.4 Symmetry, QuasiSymmetry, and QuasiIndependence,
 426
 11.5 Measuring Agreement Between Observers,
 432
 11.6 Bradley Terry Model for Paired Preferences,
 436
 11.7 Marginal Models and QuasiSymmetry Models for Matched Sets,
 439
 Notes,
 443
 Exercises,
 445
 12 Clustered Categorical Data: Marginal and Transitional Models
 455
 12.1 Marginal Modeling: Maximum Likelihood Approach,
 456
 12.2 Marginal Modeling: Generalized Estimating Equations (GEEs) Approach,
 462
 12.3 QuasiLikelihood and Its GEE Multivariate Extension: Details,
 465
 12.4 Transitional Models: Markov Chain and Time Series Models,
 473
 Notes,
 478
 Exercises,
 479
 13 Clustered Categorical Data: Random Effects Models
 489
 13.1 Random Effects Modeling of Clustered Categorical Data,
 489
 13.2 Binary Responses: LogisticNormal Model,
 494
 13.3 Examples of Random Effects Models for Binary Data,
 498
 13.4 Random Effects Models for Multinomial Data,
 511
 13.5 Multilevel Modeling,
 515
 13.6 GLMM Fitting, Inference, and Prediction,
 519
 13.7 Bayesian Multivariate Categorical Modeling,
 523
 Notes,
 525
 Exercises,
 527
 14 Other Mixture Models for Discrete Data
 535
 14.1 Latent Class Models,
 535
 14.2 Nonparametric Random Effects Models,
 542
 14.3 BetaBinomial Models,
 548
 14.4 Negative Binomial Regression,
 552
 14.5 Poisson Regression with Random Effects,
 555
 Notes,
 557
 Exercises,
 558
 15 NonModelBased Classification and Clustering
 565
 15.1 Classification: Linear Discriminant Analysis,
 565
 15.2 Classification: TreeStructured Prediction,
 570
 15.3 Cluster Analysis for Categorical Data,
 576
 Notes,
 581
 Exercises,
 582
 16 Large and SmallSample Theory for Multinomial Models
 587
 16.1 Delta Method,
 587
 16.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities,
 592
 16.3 Asymptotic Distributions of Residuals and Goodnessoffit Statistics,
 594
 16.4 Asymptotic Distributions for Logit/Loglinear Models,
 599
 16.5 SmallSample Significance Tests for Contingency Tables,
 601
 16.6 SmallSample Confidence Intervals for Categorical Data,
 603
 16.7 Alternative Estimation Theory for Parametric Models,
 610
 Notes,
 615
 Exercises,
 616
 17 Historical Tour of Categorical Data Analysis
 623
 17.1 Pearson Yule Association Controversy,
 623
 17.2 R. A. Fisher s Contributions,
 625
 17.3 Logistic Regression,
 627
 17.4 Multiway Contingency Tables and Loglinear Models,
 629
 17.5 Bayesian Methods for Categorical Data,
 633
 17.6 A Look Forward, and Backward,
 634
 Appendix A Statistical Software for Categorical Data Analysis
 637
 Appendix B ChiSquared Distribution Values
 641
 References
 643
 Author Index
 689
 Example Index
 701
 Subject Index
 705
 Appendix C Software Details for Text Examples (text website).
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QA278 .A353 2013  Unknown 
QA278 .A353 2013  Unknown 
6. Using multivariate statistics [2013]
 Tabachnick, Barbara G., 1936
 6th ed.  Boston : Pearson Education, c2013.
 Description
 Book — xxxi, 983 p. : ill. ; 24 cm.
 Summary

 In this Section:
 1. Brief Table of Contents
 2. Full Table of Contents
 1. BRIEF TABLE OF CONTENTS
 Chapter 1 Introduction
 Chapter 2 A Guide to Statistical Techniques: Using the Book
 Chapter 3 Review of Univariate and Bivariate Statistics
 Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis
 Chapter 5 Multiple Regression
 Chapter 6 Analysis of Covariance
 Chapter 7 Multivariate Analysis of Variance and Covariance
 Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures
 Chapter 9 Discriminant Analysis
 Chapter 10 Logistic Regression
 Chapter 11 Survival/Failure Analysis
 Chapter 12 Canonical Correlation
 Chapter 13 Principal Components and Factor Analysis
 Chapter 14 Structural Equation Modeling
 Chapter 15 Multilevel Linear Modeling
 Chapter 16 Multiway Frequency Analysis
 2. FULL TABLE OF CONTENTS
 Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book
 Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data
 Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. ChiSquare Analysis
 Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening
 Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs
 Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs
 Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs
 Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs
 Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs
 Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs
 Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs
 Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs
 Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs
 Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs
 Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs
 Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs.
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QA278 .T3 2013  Unknown 
7. Estadística multivariada [2011]
 Lacourly, Nancy, author.
 [Santiago de Chile] : J.C. Sáez Editor, [2011]
 Description
 Book — 1 online resource (195 pages) : illustrations.
 Shirali, Satish, 1940
 London ; New York : Springer, c2011.
 Description
 Book — ix, 394 p.
 Online

 dx.doi.org SpringerLink
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9. Analysis of ordinal categorical data [2010]
 Agresti, Alan.
 2nd ed.  Hoboken, N.J. : Wiley, c2010.
 Description
 Book — xi, 396 p. : ill. ; 24 cm.
 Summary

 Preface.
 1. Introduction. 1.1. Ordinal Categorical Scales. 1.2. Advantages of Using Ordinal Methods. 1.3. Ordinal Modeling Versus Ordinary Regession Analysis. 1.4. Organization of This Book.
 2. Ordinal Probabilities, Scores, and Odds Ratios. 2.1. Probabilities and Scores for an Ordered Categorical Scale. 2.2. Ordinal Odds Ratios for Contingency Tables. 2.3. Confidence Intervals for Ordinal Association Measures. 2.4. Conditional Association in ThreeWay Tables. 2.5. Category Choice for Ordinal Variables. Chapter Notes. Exercises.
 3. Logistic Regression Models Using Cumulative Logits. 3.1. Types of Logits for An Ordinal Response. 3.2. Cumulative Logit Models. 3.3. Proportional Odds Models: Properties and Interpretations. 3.4. Fitting and Inference for Cumulative Logit Models. 3.5. Checking Cumulative Logit Models. 3.6. Cumulative Logit Models Without Proportional Odds. 3.7. Connections with Nonparametric Rank Methods. Chapter Notes. Exercises.
 4. Other Ordinal Logistic Regression Models. 4.1. AdjacentCategories Logit Models. 4.2. ContinuationRatio Logit Models. 4.3. Stereotype Model: Multiplicative PairedCategory Logits. Chapter Notes. Exercises.
 5. Other Ordinal Multinomial Response Models. 5.1. Cumulative Link Models. 5.2. Cumulative Probit Models. 5.3. Cumulative LogLog Links: Proportional Hazards Modeling. 5.4. Modeling Location and Dispersion Effects. 5.5. Ordinal ROC Curve Estimation. 5.6. Mean Response Models. Chapter Notes. Exercises.
 6. Modeling Ordinal Association Structure. 6.1. Ordinary Loglinear Modeling. 6.2. Loglinear Model of LinearbyLinear Association. 6.3. Row or Column Effects Association Models. 6.4. Association Models for Multiway Tables. 6.5. Multiplicative Association and Correlation Models. 6.6. Modeling Global Odds Ratios and Other Associations. Chapter Notes. Exercises.
 7. NonModelBased Analysis of Ordinal Association. 7.1. Concordance and Discordance Measures of Association. 7.2. Correlation Measures for Contingency Tables. 7.3. NonModelBased Inference for Ordinal Association Measures. 7.4. Comparing Singly Ordered Multinomials. 7.5. OrderRestricted Inference with Inequality Constraints. 7.6. SmallSample Ordinal Tests of Independence. 7.7. Other RankBased Statistical Methods for Ordered Categories. Appendix: Standard Errors for Ordinal Measures. Chapter Notes. Exercises.
 8. MatchedPairs Data with Ordered Categories. 8.1. Comparing Marginal Distributions for Matched Pairs. 8.2. Models Comparing Matched Marginal Distributions. 8.3. Models for The Joint Distribution in A Square Table. 8.4. Comparing Marginal Distributions for Matched Sets. 8.5. Analyzing Rater Agreement on an Ordinal Scale. 8.6. Modeling Ordinal Paired Preferences. Chapter Notes. Exercises.
 9. Clustered Ordinal Responses: Marginal Models. 9.1. Marginal Ordinal Modeling with Explanatory Variables. 9.2. Marginal Ordinal Modeling: GEE Methods. 9.3. Transitional Ordinal Modeling, Given the Past. Chapter Notes. Exercises.
 10. Clustered Ordinal Responses: Random Effects Models. 10.1. Ordinal Generalized Linear Mixed Models. 10.2. Examples of Ordinal Random Intercept Models. 10.3. Models with Multiple Random Effects. 10.4. Multilevel (Hierarchical) Ordinal Models. 10.5. Comparing Random Effects Models and Marginal Models. Chapter Notes. Exercises.
 11. Bayesian Inference for Ordinal Response Data. 11.1. Bayesian Approach to Statistical Inference. 11.2. Estimating Multinomial Parameters. 11.3. Bayesian Ordinal Regression Modeling. 11.4. Bayesian Ordinal Association Modeling. 11.5. Bayesian Ordinal Multivariate Regression Modeling. 11.6. Bayesian Versus Frequentist Approaches to Analyzing Ordinal Data. Chapter Notes. Exercises. Appendix Software for Analyzing Ordinal Categorical Data. Bibliography. Example Index. Subject Index.
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QA278 .A35 2010  Unknown 
 Le, Chap T., 1948
 2nd ed.  Hoboken, N.J. : John Wiley & Sons, c2010.
 Description
 Book — xv, 399 : ill. ; 24 cm.
 Summary

 Preface. Preface to the First Edition.
 1 Introduction. 1.1 A Prototype Example. 1.2 A Review of LikelihoodBased Methods. 1.3 Interval Estimation for a Proportion. 1.4 About This Book.
 2 Contingency Tables. 2.1 Some Sampling Models for Categorical Data. 2.1.1 The Binomial and Multinomial Distributions. 2.1.2 The Hypergeometric Distributions. 2.2 Inferences for 2by2 Contingency Tables. 2.2.1 Comparison of Two Proportions. 2.2.2 Tests for Independence. 2.2.3 Fisher's Exact Test. 2.2.4 Relative Risk and Odds Ratio. 2.2.5 Etiologic Fraction. 2.2.6 Crossover Designs. 2.3 The MantelHaenszel Method. 2.4 Inferences for General TwoWay Tables. 2.4.1 Comparison of Several Proportions. 2.4.2 Testing for Independence in TwoWay Tables. 2.4.3 Ordered 2byk Contingency Tables. 2.5 Sample Size Determination. Exercises.
 3 Loglinear Models. 3.1 Loglinear Models for TwoWay Tables. 3.2 Loglinear Models for ThreeWay Tables. 3.2.1 The Models of Independence. 3.2.2 Relationships Between Terms and Hierarchy of Models. 3.2.3 Testing a Specific Model. 3.2.4 Searching for the Best Model. 3.2.5 Collapsing Tables. 3.3 Loglinear Models for HigherDimensional Tables. 3.3.1 Testing a Specific Model. 3.3.2 Searching for the Best Model. 3.3.3 Measures of Association with an Effect Modification. 3.3.4 Searching for a Model with a Dependent Variable. Exercises.
 4 Logistic Regression Models. 4.1 Modeling a Probability. 4.1.1 The Logarithmic Transformation. 4.1.2 The Probit Transformation. 4.1.3 The Logistic Transformation. 4.2 Simple Regression Analysis. 4.2.1 The Logistic Regression Model. 4.2.2 Measure of Association. 4.2.3 Tests of Association. 4.2.4 Use of the Logistic Model for Different Designs. 4.2.5 Overdispersion. 4.3 Multiple Regression Analysis. 4.3.1 Logistic Regression Model with Several Covariates. 4.3.2 Effect Modifications. 4.3.3 Polynomial Regression. 4.3.4 Testing Hypotheses in Multiple Logistic Regression. 4.3.5 Measures of GoodnessofFit. 4.4 Ordinal Logistic Model. 4.5 Quantal Bioassays. 4.5.1 Types of Bioassays. 4.5.2 Quantal Response Bioassays. Exercises.
 5 Methods for Matched Data. 5.1 Measuring Agreement. 5.2 PairMatched CaseControl Studies. 5.2.1 The Model. 5.2.2 The Analysis. 5.2.3 The Case of Small Samples. 5.2.4 Risk Factors with Multiple Categories and Ordinal Risks. 5.3 Multiple Matching. 5.3.1 The Conditional Approach. 5.3.2 Estimation of the Odds Ratio. 5.3.3 Testing for Exposure Effect. 5.3.4 Testing for Homogeneity. 5.4 Conditional Logistic Regression. 5.4.1 Simple Regression Analysis. 5.4.2 Multiple Regression Analysis. Exercises.
 6 Methods for Count Data. 6.1 The Poisson Distribution. 6.2 Testing GoodnessofFit. 6.3 The Poisson Regression Model. 6.3.1 Simple Regression Analysis. 6.3.2 Multiple Regression Analysis. 6.3.3 Overdispersion. 6.3.4 Stepwise Regression. Exercise.
 7 Categorical Data and Translational Research. 7.1 Types of Clinical Studies. 7.2 From Bioassays to Translational Research. 7.2.1 Analysis of In Vitro Experiments. 7.2.2 Design and Analysis of Experiments for Combination Therapy. 7.3 Phase I Clinical Trials. 7.3.1 Standard Design. 7.3.2 Fast Track Design. 7.3.3 Continual Reassessment Method. 7.4 Phase II Clinical Trials. 7.4.1 Sample Size Determination for Phase II Clinical Trials. 7.4.2 Phase II Clinical Trial Designs for Selection. 7.4.3 TwoStage Phase II Design. 7.4.4 Toxicity Monitoring in Phase II Trials. 7.4.5 Multiple Decisions. Exercises.
 8 Categorical Data and Diagnostic Medicine. 8.1 Some Examples. 8.2 The Diagnosis Process. 8.2.1 The Developmental Stage. 8.2.2 The Applicational Stage. 8.3 Some Statistical Issues. 8.3.1 The Response Rate. 8.3.2 The Issue of Population Random Testing. 8.3.3 Screenable Disease Prevalence. 8.3.4 An Index for Diagnostic Competence. 8.4 Prevalence Surveys. 8.4.1 Known Sensitivity and Specificity. 8.4.2 Unknown Sensitivity and Specificity. 8.4.3 Prevalence Survey with a New Test. 8.5 The Receiver Operating Characteristic Curve. 8.5.1 The ROC Function and ROC Curve. 8.5.2 Some Parametric ROC Models. 8.5.3 Estimation of the ROC Curve. 8.5.4 Index for Diagnostic Accuracy. 8.5.5 Estimation of Area Under ROC Curve. 8.6 The Optimization Problem. 8.6.1 Basic Criterion: Youden's Index. 8.6.2 Possible Solutions. 8.7 Statistical Considerations. 8.7.1 Evaluation of Screening Tests. 8.7.2 Comparison of Screening Tests. 8.7.3 Consideration of Subjects' Characteristics. Exercises.
 9 Transition from Categorical to Survival Data. 9.1 Survival Data. 9.2 Introductory Survival Analysis. 9.2.1 KaplanMeier Curve. 9.2.2 Comparison of Survival Distributions. 9.3 Simple Regression and Correlation. 9.3.1 Model and Approach. 9.3.2 Measures of Association. 9.3.3 Tests of Association. 9.4 Multiple Regression and Correlation. 9.4.1 Proportional Hazards Models with Several Covariates. 9.4.2 Testing Hypotheses in Multiple Regression. 9.4.3 TimeDependent Covariates and Applications. 9.5 Competing Risks. 9.5.1 Redistribution to the Right Method. 9.5.2 Estimation of the Cumulative Incidence. 9.5.3 Brief Discussion of Proportional Hazards Regression. Exercise. Bibliography. Index.
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QA278 .L39 2010  Unknown 
 Singapore ; Hackensack, N.J. : World Scientific Pub. Co., c2009.
 Description
 Book — xiv, 477 p. : ill. (some col.).
 Summary

 HighDimensional Discrete Statistical Models Multivariate Theory for HighDimensional Data with Fewer Observations Modelbased Penalized Clustering for Multivariate Data Jacobians under Constraints and Statistical Bioinformatics Cluster Validation for Microarray Data Flexible Bivariate Circular Models Optimal Text Space Representation Through Circular Data Analysis Linear Regression for Random Measures Mixed Multivariate Models for Random Sums and Maxima Estimation of the BoxCox Transformation Parameters Generation of Multivariate Densities Smooth Estimation of Multivariate Distribution and Density and Functions Estimation Using Quantile Function Structure Optimal Estimating Functions in the Presence of Nuisance Parameters Inference in Exponential Family Regression Models Under Shape Constraints Optimal Adaptive Rule in Testing Problem Robust Tests for Inverse Gaussian Scale Parameters Clusterwise Regression Using Dirichlet Mixtures Bayesian Analysis of Rank Data Bayesian Tests of Equality of Stratified Proportions for a MultipleResponse Categorical Variable RespondentGenerated Intervals in Sample Surveys Quality Index and Mahalanobis D2 Statistic AQLbased Multiattribute Sampling Scheme Multivariate Quality Management Large Time Series of Categorical Data Estimation of Integrated Covolatility for Asynchronous Assets Improving the HansenHurwitz Estimator in PPSWR Sampling.
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12. Multivariate statistical analysis [2009]
 Mukhopadhyay, Parimal.
 New Jersey : World Scientific, c2009.
 Description
 Book — xviii, 549 p. : ill. ; 24 cm.
 Summary

 Organization of the Multivariate Data, Measures of Distance, Treatment of Missing Observations Multivariate Normal Distribution and Related Distributions (Wishart, Hotelling's T2, Wilks') Tests for Multivariate Normality, Robust Estimation of Location and Scale Parameters Testing of Multivariate Hypotheses, Simultaneous Confidence Intervals Multivariate Regression Analysis Analysis of Variance and Covariance Principal Component Analysis Factor Analysis Canonical Correlation Classification and Discrimination.
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QA278 .M8565 2009  Unknown 
 Raykov, Tenko.
 New York : Routledge, c2008.
 Description
 Book — x, 485 p. : ill. ; 24 cm.
 Summary

This comprehensive new text introduces readers to the most commonly used multivariate techniques at an introductory, nontechnical level. By focusing on the fundamentals, readers are better prepared for more advanced applied pursuits, particularly on topics that are most critical to the behavioural, social, and educational sciences. Analogies between the already familiar univariate statistics and multivariate statistics are emphasized throughout. The authors examine in detail how each multivariate technique can be implemented using SPSS and SAS and Mplus in the book's later chapters. Important assumptions are discussed along the way along with tips for how to deal with pitfalls the reader may encounter. Mathematical formulas are used only in their definitional meaning rather than as elements of formal proofs.A book specific website provides files with all of the data used in the text so readers can replicate the results. The Appendix explains the data files and its variables. The software code (for SAS and Mplus) and the menu option selections for SPSS are also discussed in the book and presented on the website. The book is distinguished by its use of latent variable modelling to address multivariate questions specific to behavioural and social scientists including missing data analysis and longitudinal data modelling. Ideal for graduate and advanced undergraduate students in the behavioural, social, and educational sciences, this book will also appeal to researchers in these disciplines who have limited familiarity with multivariate statistics. Recommended prerequisites include an introductory statistics course with exposure to regression analysis and some familiarity with SPSS and SAS.
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QA278 .R394 2008  Unknown 
14. Multiparametric statistics [2008]
 Serdobolskii, V.
 1st ed.  Amsterdam ; Boston : Elsevier, 2008.
 Description
 Book — xvii, 315 p. ; 24 cm.
 Summary

 Foreword Preface
 Chapter 1. Introduction: The Development of Multiparametric Statistics
 Chapter 2. Fundamental Problem of Statistics
 Chapter 3. Spectral Theory of Large Sample Covariance Matrices
 Chapter 4. Asymptitically Unimprovable Solution of Multivariate Problems
 Chapter 5. Multiparametric Discriminant Analysis
 Chapter 6. Theory of Solution to HighOrder Systems of Empirical Linear Algebraic Equations Appendix References Index.
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QA278 .S47 2008  Available 
 Powers, Daniel A.
 2nd ed.  Bingley, UK : Emerald, 2008.
 Description
 Book — xvii, 317 p. : ill. ; 25 cm.
 Summary

 Review of linear regression models
 Models for binary data
 Loglinear models for contingency tables
 Multilevel models for binary data
 Statistical models for event occurrence
 Models for ordinal dependent variables
 Models for nominal dependent variables.
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA278 .P59 2008  Unknown 
16. Tractability of multivariate problems [2008  2012]
 Novak, Erich, 1953
 Zürich : European Mathematical Society, ©2008©2012.
 Description
 Book — 3v. ; 25 cm.
 Summary

 v. 1. Linear information
 v. 2. Standard information for functionals
 v. 3.Standard information for operators.
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks


QA278 .N68 2008 V.1  Unknown 
QA278 .N68 2008 V.2  Unknown 
QA278 .N68 2008 V.3  Unknown 
17. Applied multivariate statistical analysis [2007]
 Johnson, Richard A. (Richard Arnold), 1937
 6th ed.  Upper Saddle River, N.J. : Pearson Prentice Hall, c2007.
 Description
 Book — xviii, 773 p. : ill. ; 25 cm.
 Summary

 DRAFT (NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED.
 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments.
 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A SquareRoot Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts.
 3. Sample Geometry and Random Sampling. The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables.
 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. LargeSample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T
 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the pDimensional Ellipsoids.
 6. Comparisons of Several Multivariate Means. Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations. Comparison of Several Multivariate Population Means (OneWay MANOVA). Simultaneous Confidence Intervals for Treatment Effects. TwoWay Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models.
 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE.
 8. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. LargeSample Inferences. Monitoring Quality with Principal Components. Supplement 8A The Geometry of the Sample Principal Component Approximation.
 9. Factor Analysis and Inference for Structured Covariance Matrices. The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Supplement 9A Some Computational Details for Maximum Likelihood Estimation.
 10. Canonical Correlation Analysis Canonical Variates and Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES.
 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments.
 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's tDistribution Percentage Points. ...c2 Distribution Percentage Points. FDistribution Percentage Points. FDistribution Percentage Points (...a = .10). FDistribution Percentage Points (...a = .05). FDistribution Percentage Points (...a = .01). Data Index. Subject Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA278 .J63 2007  Unknown 
18. Applied multivariate statistical analysis [2007]
 Härdle, Wolfgang.
 2nd ed.  Berlin ; New York : Springer, c2007.
 Description
 Book — xii, 458 p. : ill ; 24 cm.
 Summary

 I Descriptive Techniques: Comparison of Batches. II Multivariate Random Variables: A Short Excursion into Matrix Algebra Moving to Higher Dimensions Multivariate Distributions Theory of the Multinormal Theory of Estimation Hypothesis Testing. III Multivariate Techniques: Decomposition of Data Matrices by Factors Principal Components Analysis Factor Analysis Cluster Analysis Discriminant Analysis. Correspondence Analysis. Canonical Correlation Analysis. Multidimensional Scaling. Conjoint Measurement Analysis. Application in Finance. Computationally Intensive Techniques. A: Symbols and Notations. B: Data. Bibliography. Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Business Library
Business Library  Status 

Stacks  
QA278 .H346 2007  Unknown 
 Härdle, Wolfgang.
 2nd ed.  Berlin ; New York : Springer, c2007.
 Description
 Book — xii, 458 p. : ill.
20. Discrete multivariate analysis [2007]
 Bishop, Yvonne M. M. (Yvonne Millicent Mahala), author.
 New York : Springer, 2007.
 Description
 Book — 1 online resource (557 pages) : illustrations
 Summary

 Structural Models For Counted Data. Maximum Likelihood Estimates For Complete Tables. Formal Goodness Of Fit: Summary Statistics And Model Selection. Maximum Likelihood Estimation For Incomplete Tables. Estimating The Size Of A Closed Population. Models For Measuring Change. Analysis Of Square Tables: Symmetry And Marginal Homogeneity. Model Selection And Assessing Closeness Of Fit: Practical Aspects. Other Methods For Estimation And Testing In CrossClassifications. Measures Of Association And Agreement. PseudoBayes Estimates Of Cell Probabilities. Sampling Models For Discrete Data. Asymptotic Methods.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
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