 Book
 xxvii, 717 p. : ill.
 Machine generated contents note: Preface xviiPreface to the Second Edition xixR software and functions xxData Sets xxiiOpen Problems in Mixed Models xxiii1 Introduction: Why Mixed Models? 11.1 Mixed effects for clustered data 21.2 ANOVA, variance components, and the mixed model 41.3 Other special cases of the mixed effects model 61.4 A compromise between Bayesian and frequentist approaches 71.5 Penalized likelihood and mixed effects 91.6 Healthy Akaike information criterion 111.7 Penalized smoothing 131.8 Penalized polynomial fitting 161.9 Restraining parameters, or what to eat 181.10 Illposed problems, Tikhonov regularization, and mixed effects 201.11 Computerized tomography and linear image reconstruction 231.12 GLMM for PET 261.13 Maple shape leaf analysis 291.14 DNA Western blot analysis 311.15 Where does the wind blow? 331.16 Software and books361.17 Summary points 372 MLE for LME Model 412.1 Example: Weight versus height 422.2 The model and loglikelihood functions 452.3 Balanced randomcoefficient model 602.4 LME model with random intercepts 642.5 Criterion for the MLE existence 722.6 Criterion for positive definiteness of matrix D742.7 Preestimation bounds for variance parameters 772.8 Maximization algorithms792.9 Derivatives of the loglikelihood function 812.10 NewtonRaphson algorithm 832.11 Fisher scoring algorithm852.12 EM algorithm 882.13 Starting point 932.14 Algorithms for restricted MLE 962.15 Optimization on nonnegative definite matrices 972.16 lmeFS and lme in R 1082.17 Appendix: Proof of the MLE existence 1122.18 Summary points 1153 Statistical Properties of the LME Model 1193.1 Introduction 1193.2 Identifiability of the LMEmodel 1193.3 Information matrix for variance parameters 1223.4 Profilelikelihood confidence intervals 1333.5 Statistical testing of the presence of random effects 1353.6 Statistical properties of MLE 1393.7 Estimation of random effects 1483.8 Hypothesis and membership testing 1533.9 Ignoring random effects 1573.10 MINQUE for variance parameters 1603.11 Method of moments 1693.12 Variance least squares estimator 1733.13 Projection on D+ space 1783.14 Comparison of the variance parameter estimation 1783.15 Asymptotically efficient estimation for [beta] 1823.16 Summary points 1834 Growth Curve Model and Generalizations 1874.1 Linear growth curve model 1874.2 General linear growth curve model 2034.3 Linear model with linear covariance structure 2214.4 Robust linear mixed effects model 2354.5 Appendix: Derivation of the MM estimator 2434.6 Summary points 2445 Metaanalysis Model 2475.1 Simple metaanalysis model 2485.2 Metaanalysis model with covariates 2755.3 Multivariate metaanalysis model 2805.4 Summary points 2916 Nonlinear Marginal Model 2936.1 Fixed matrix of random effects 2946.2 Varied matrix of random effects 3076.3 Three types of nonlinear marginal models 3186.4 Total generalized estimating equations approach 3236.5 Summary points 3307 Generalized Linear Mixed Models 3337.1 Regression models for binary data 3347.2 Binary model with subjectspecific intercept 3577.3 Logistic regression with random intercept 3647.4 Probit model with random intercept 3847.5 Poisson model with random intercept 3887.6 Random intercept model: overview 4037.7 Mixed models with multiple random effects 4047.8 GLMM and simulation methods 4137.9 GEE for clustered marginal GLM 4187.10 Criteria for MLE existence for binary model 4267.11 Summary points 4318 Nonlinear Mixed Effects Model 4358.1 Introduction 4358.2 The model 4368.3 Example: Height of girls and boys 4398.4 Maximum likelihood estimation 4418.5 Twostage estimator 4448.6 Firstorder approximation 4508.7 LindstromBates estimator 4528.8 Likelihood approximations 4578.9 Oneparameter exponential model 4608.10 Asymptotic equivalence of the TS and LB estimators 4678.11 Biascorrected twostage estimator 4698.12 Distribution misspecification 4718.13 Partially nonlinear marginal mixed model 4748.14 Fixed sample likelihood approach4758.15 Estimation of random effects and hypothesis testing 4788.16 Example (continued) 4798.17 Practical recommendations 4818.18 Appendix: Proof of theorem on equivalence 4828.19 Summary points 4859 Diagnostics and Influence Analysis 4899.1 Introduction 4899.2 Influence analysis for linear regression 4909.3 The idea of infinitesimal influence 4939.4 Linear regression model 4959.5 Nonlinear regression model 5129.6 Logistic regression for binary outcome 5179.7 Influence of correlation structure 5269.8 Influence of measurement error 5279.9 Influence analysis for the LME model 5309.10 Appendix: MLE derivative with respect to σ 2 5369.11 Summary points 53710 Tumor Regrowth Curves 54110.1 Survival curves 54310.2 Doubleexponential regrowth curve 54510.3 Exponential growth with fixed regrowth time 55910.4 General regrowth curve 56510.5 Doubleexponential transient regrowth curve 56610.6 Gompertz transient regrowth curve 57310.7 Summary points 57611 Statistical Analysis of Shape 57911.1 Introduction 57911.2 Statistical analysis of random triangles 58111.3 Face recognition 58411.4 Scaleirrelevant shape model 58511.5 Gorilla vertebrae analysis 58911.6 Procrustes estimation of the mean shape 59111.7 Fourier descriptor analysis 59811.8 Summary points 60712 Statistical Image Analysis 60912.1 Introduction 60912.2 Testing for uniform lighting 61212.3 KolmogorovSmirnov image comparison 61612.4 Multinomial statistical model for images 62012.5 Image entropy 62312.6 Ensemble of unstructured images 62712.7 Image alignment and registration 64012.8 Ensemble of structured images 65212.9 Modeling spatial correlation 65412.10 Summary points 66013 Appendix: Useful Facts and Formulas 66313.1 Basic facts of asymptotic theory 66313.2 Some formulas of matrix algebra 67013.3 Basic facts of optimization theory 674References 683Index 713.
 Machine generated contents note: Preface xviiPreface to the Second Edition xixR software and functions xxData Sets xxiiOpen Problems in Mixed Models xxiii1 Introduction: Why Mixed Models? 11.1 Mixed effects for clustered data 21.2 ANOVA, variance components, and the mixed model 41.3 Other special cases of the mixed effects model 61.4 A compromise between Bayesian and frequentist approaches 71.5 Penalized likelihood and mixed effects 91.6 Healthy Akaike information criterion 111.7 Penalized smoothing 131.8 Penalized polynomial fitting 161.9 Restraining parameters, or what to eat 181.10 Illposed problems, Tikhonov regularization, and mixed effects 201.11 Computerized tomography and linear image reconstruction 231.12 GLMM for PET 261.13 Maple shape leaf analysis 291.14 DNA Western blot analysis 311.15 Where does the wind blow? 331.16 Software and books361.17 Summary points 372 MLE for LME Model 412.1 Example: Weight versus height 422.2 The model and loglikelihood functions 452.3 Balanced randomcoefficient model 602.4 LME model with random intercepts 642.5 Criterion for the MLE existence 722.6 Criterion for positive definiteness of matrix D742.7 Preestimation bounds for variance parameters 772.8 Maximization algorithms792.9 Derivatives of the loglikelihood function 812.10 NewtonRaphson algorithm 832.11 Fisher scoring algorithm852.12 EM algorithm 882.13 Starting point 932.14 Algorithms for restricted MLE 962.15 Optimization on nonnegative definite matrices 972.16 lmeFS and lme in R 1082.17 Appendix: Proof of the MLE existence 1122.18 Summary points 1153 Statistical Properties of the LME Model 1193.1 Introduction 1193.2 Identifiability of the LMEmodel 1193.3 Information matrix for variance parameters 1223.4 Profilelikelihood confidence intervals 1333.5 Statistical testing of the presence of random effects 1353.6 Statistical properties of MLE 1393.7 Estimation of random effects 1483.8 Hypothesis and membership testing 1533.9 Ignoring random effects 1573.10 MINQUE for variance parameters 1603.11 Method of moments 1693.12 Variance least squares estimator 1733.13 Projection on D+ space 1783.14 Comparison of the variance parameter estimation 1783.15 Asymptotically efficient estimation for [beta] 1823.16 Summary points 1834 Growth Curve Model and Generalizations 1874.1 Linear growth curve model 1874.2 General linear growth curve model 2034.3 Linear model with linear covariance structure 2214.4 Robust linear mixed effects model 2354.5 Appendix: Derivation of the MM estimator 2434.6 Summary points 2445 Metaanalysis Model 2475.1 Simple metaanalysis model 2485.2 Metaanalysis model with covariates 2755.3 Multivariate metaanalysis model 2805.4 Summary points 2916 Nonlinear Marginal Model 2936.1 Fixed matrix of random effects 2946.2 Varied matrix of random effects 3076.3 Three types of nonlinear marginal models 3186.4 Total generalized estimating equations approach 3236.5 Summary points 3307 Generalized Linear Mixed Models 3337.1 Regression models for binary data 3347.2 Binary model with subjectspecific intercept 3577.3 Logistic regression with random intercept 3647.4 Probit model with random intercept 3847.5 Poisson model with random intercept 3887.6 Random intercept model: overview 4037.7 Mixed models with multiple random effects 4047.8 GLMM and simulation methods 4137.9 GEE for clustered marginal GLM 4187.10 Criteria for MLE existence for binary model 4267.11 Summary points 4318 Nonlinear Mixed Effects Model 4358.1 Introduction 4358.2 The model 4368.3 Example: Height of girls and boys 4398.4 Maximum likelihood estimation 4418.5 Twostage estimator 4448.6 Firstorder approximation 4508.7 LindstromBates estimator 4528.8 Likelihood approximations 4578.9 Oneparameter exponential model 4608.10 Asymptotic equivalence of the TS and LB estimators 4678.11 Biascorrected twostage estimator 4698.12 Distribution misspecification 4718.13 Partially nonlinear marginal mixed model 4748.14 Fixed sample likelihood approach4758.15 Estimation of random effects and hypothesis testing 4788.16 Example (continued) 4798.17 Practical recommendations 4818.18 Appendix: Proof of theorem on equivalence 4828.19 Summary points 4859 Diagnostics and Influence Analysis 4899.1 Introduction 4899.2 Influence analysis for linear regression 4909.3 The idea of infinitesimal influence 4939.4 Linear regression model 4959.5 Nonlinear regression model 5129.6 Logistic regression for binary outcome 5179.7 Influence of correlation structure 5269.8 Influence of measurement error 5279.9 Influence analysis for the LME model 5309.10 Appendix: MLE derivative with respect to σ 2 5369.11 Summary points 53710 Tumor Regrowth Curves 54110.1 Survival curves 54310.2 Doubleexponential regrowth curve 54510.3 Exponential growth with fixed regrowth time 55910.4 General regrowth curve 56510.5 Doubleexponential transient regrowth curve 56610.6 Gompertz transient regrowth curve 57310.7 Summary points 57611 Statistical Analysis of Shape 57911.1 Introduction 57911.2 Statistical analysis of random triangles 58111.3 Face recognition 58411.4 Scaleirrelevant shape model 58511.5 Gorilla vertebrae analysis 58911.6 Procrustes estimation of the mean shape 59111.7 Fourier descriptor analysis 59811.8 Summary points 60712 Statistical Image Analysis 60912.1 Introduction 60912.2 Testing for uniform lighting 61212.3 KolmogorovSmirnov image comparison 61612.4 Multinomial statistical model for images 62012.5 Image entropy 62312.6 Ensemble of unstructured images 62712.7 Image alignment and registration 64012.8 Ensemble of structured images 65212.9 Modeling spatial correlation 65412.10 Summary points 66013 Appendix: Useful Facts and Formulas 66313.1 Basic facts of asymptotic theory 66313.2 Some formulas of matrix algebra 67013.3 Basic facts of optimization theory 674References 683Index 713.
 Book
 xiii, 287 p. : ill.
3. Analysis of variance and covariance : how to choose and construct models for the life sciences [2007]
 Book
 xiii, 287 p. : ill. ; 24 cm.
 Preface Introduction to analysis of variance Introduction to model structures Part I. Model Structures: 1. Onefactor designs 2. Nested designs 3. Fully replicated factorial designs 4. Randomised block designs 5. Split plot designs 6. Repeated measures designs 7. Unreplicated designs Part II. Further Topics: 8. Further topics 9. Choosing experimental designs 10. Best practice in presentation of the design 11. Troubleshooting problems during analysis Glossary Categories of model Bibliography Index of all ANOVA models with up to three factors Index.
 (source: Nielsen Book Data)9780521684477 20160528
(source: Nielsen Book Data)9780521684477 20160528
 Preface Introduction to analysis of variance Introduction to model structures Part I. Model Structures: 1. Onefactor designs 2. Nested designs 3. Fully replicated factorial designs 4. Randomised block designs 5. Split plot designs 6. Repeated measures designs 7. Unreplicated designs Part II. Further Topics: 8. Further topics 9. Choosing experimental designs 10. Best practice in presentation of the design 11. Troubleshooting problems during analysis Glossary Categories of model Bibliography Index of all ANOVA models with up to three factors Index.
 (source: Nielsen Book Data)9780521684477 20160528
(source: Nielsen Book Data)9780521684477 20160528
site.ebrary.com ebrary
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QH323.5 .D66 2007  Available 
 Book
 xxv, 480 p. : ill.
 v. 1. Balanced data
 v. 2. Unbalanced data.
 v. 1. Balanced data
 v. 2. Unbalanced data.
5. Analysis of variance for random models : theory, methods, applications and data analysis [2004  ]
 Book
 v : ill. ; 25. cm.
 v. 1. Balanced data
 v. 2. Unbalanced data.
(source: Nielsen Book Data)9780817632304 20160528
Analysis of variance (ANOVA) models have become widely used tools and play a fundamental role in much of the application of statistics today. In particular, ANOVA models involving random effects have found widespread application to experimental design in a variety of fields requiring measurements of variance, including agriculture, biology, animal breeding, applied genetics, econometrics, quality control, medicine, engineering, and social sciences.This twovolume work is a comprehensive presentation of different methods and techniques for point estimation, interval estimation, and tests of hypotheses for linear models involving random effects. Both Bayesian and repeated sampling procedures are considered. Volume 1 examines models with balanced data (orthogonal models); Volume 2 studies models with unbalanced data (nonorthogonal models).Accessible to readers with only a modest mathematical and statistical background, the work will appeal to a broad audience of students, researchers, and practitioners in the mathematical, life, social, and engineering sciences. It may be used as a textbook in upperlevel undergraduate and graduate courses, or as a reference for readers interested in the use of random effects models for data analysis.
(source: Nielsen Book Data)9780817632298 20160528
 v. 1. Balanced data
 v. 2. Unbalanced data.
(source: Nielsen Book Data)9780817632304 20160528
Analysis of variance (ANOVA) models have become widely used tools and play a fundamental role in much of the application of statistics today. In particular, ANOVA models involving random effects have found widespread application to experimental design in a variety of fields requiring measurements of variance, including agriculture, biology, animal breeding, applied genetics, econometrics, quality control, medicine, engineering, and social sciences.This twovolume work is a comprehensive presentation of different methods and techniques for point estimation, interval estimation, and tests of hypotheses for linear models involving random effects. Both Bayesian and repeated sampling procedures are considered. Volume 1 examines models with balanced data (orthogonal models); Volume 2 studies models with unbalanced data (nonorthogonal models).Accessible to readers with only a modest mathematical and statistical background, the work will appeal to a broad audience of students, researchers, and practitioners in the mathematical, life, social, and engineering sciences. It may be used as a textbook in upperlevel undergraduate and graduate courses, or as a reference for readers interested in the use of random effects models for data analysis.
(source: Nielsen Book Data)9780817632298 20160528
Vol. 1 SpringerLink
 Vol. 1 SpringerLink
 Vol. 2 SpringerLink
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 Vol. 2 ebrary
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6. Mixed models : theory and applications [2004]
 Book
 xviii, 704 p. : ill. ; 25 cm.
 Preface.1. Introduction: Why Mixed Models?2. MLE for LME Model.3. Statistical Properties of the LME Model.4. Growth Curve Model and Generalizations.5. Metaanalysis Model.6. Nonlinear Marginal Model.7. Generalized Linear Mixed Models.8. Nonlinear Mixed Effects Model.9. Diagnostics and Influence Analysis.10. Tumor Regrowth Curves.11. Statistical Analysis of Shape.12. Statistical Image Analysis.13. Appendix: Useful Facts and Formulas.References.Index.
 (source: Nielsen Book Data)9780471601616 20160528
(source: Nielsen Book Data)9780471601616 20160528
 Preface.1. Introduction: Why Mixed Models?2. MLE for LME Model.3. Statistical Properties of the LME Model.4. Growth Curve Model and Generalizations.5. Metaanalysis Model.6. Nonlinear Marginal Model.7. Generalized Linear Mixed Models.8. Nonlinear Mixed Effects Model.9. Diagnostics and Influence Analysis.10. Tumor Regrowth Curves.11. Statistical Analysis of Shape.12. Statistical Image Analysis.13. Appendix: Useful Facts and Formulas.References.Index.
 (source: Nielsen Book Data)9780471601616 20160528
(source: Nielsen Book Data)9780471601616 20160528
dx.doi.org Wiley Online Library
 dx.doi.org Wiley Online Library
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7. Plane Answers to Complex Questions [2002]
 Book
 1 online resource (487 pages)
 Introduction * Estimation * Testing Hypotheses * OneWay ANOVA * Multiple Comparison Techniques * Regression Analysis * Multifactor Analysis of Variance * Experimental Design Models * Analysis of Covariance * Estimation and Testing in General GaussMarkov Models * Split Plot Models * Mixed Models and Variance Components * Checking Assumptions, Residuals, and Influential Observations * Variable Selection and Collinearity.
 (source: Nielsen Book Data)9780387953618 20180521
(source: Nielsen Book Data)9780387953618 20180521
 Introduction * Estimation * Testing Hypotheses * OneWay ANOVA * Multiple Comparison Techniques * Regression Analysis * Multifactor Analysis of Variance * Experimental Design Models * Analysis of Covariance * Estimation and Testing in General GaussMarkov Models * Split Plot Models * Mixed Models and Variance Components * Checking Assumptions, Residuals, and Influential Observations * Variable Selection and Collinearity.
 (source: Nielsen Book Data)9780387953618 20180521
(source: Nielsen Book Data)9780387953618 20180521
 Book
 xii, 180 p. : ill. ; 24 cm.
 Introduction The Need for Analysis of Variance (ANOVA) Means, Variances, Sums of Squares and Degrees of Freedom Independent Group ANOVAs OneFactor Independent Groups ANOVA Multiple Comparisons: Independent Groups tTests TwoFactor Independent Groups ANOVA Repeated Measures ANOVAs OneFactor Repeated Measures ANOVA Multiple Comparisons: Dependent Measures tTests TwoFactor Mixed Measures ANOVA TwoFactor Repeated Measures ANOVA Overview and Final Thoughts Some Tips for Tests on ANOVA Every Day Benefits of a Feel for Statistics and for Evaluating Data.
 (source: Nielsen Book Data)9780803970755 20160618
(source: Nielsen Book Data)9780803970755 20160618
 Introduction The Need for Analysis of Variance (ANOVA) Means, Variances, Sums of Squares and Degrees of Freedom Independent Group ANOVAs OneFactor Independent Groups ANOVA Multiple Comparisons: Independent Groups tTests TwoFactor Independent Groups ANOVA Repeated Measures ANOVAs OneFactor Repeated Measures ANOVA Multiple Comparisons: Dependent Measures tTests TwoFactor Mixed Measures ANOVA TwoFactor Repeated Measures ANOVA Overview and Final Thoughts Some Tips for Tests on ANOVA Every Day Benefits of a Feel for Statistics and for Evaluating Data.
 (source: Nielsen Book Data)9780803970755 20160618
(source: Nielsen Book Data)9780803970755 20160618
Law Library (Crown)
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QA279 .T86 2001  Unknown 
9. Introduction to analysis of variance [electronic resource] : design, analysis, & interpretation [2001]
 Book
 1 online resource (xii, 180 p.) : ill.
 Introduction The Need for Analysis of Variance (ANOVA) Means, Variances, Sums of Squares and Degrees of Freedom Independent Group ANOVAs OneFactor Independent Groups ANOVA Multiple Comparisons: Independent Groups tTests TwoFactor Independent Groups ANOVA Repeated Measures ANOVAs OneFactor Repeated Measures ANOVA Multiple Comparisons: Dependent Measures tTests TwoFactor Mixed Measures ANOVA TwoFactor Repeated Measures ANOVA Overview and Final Thoughts Some Tips for Tests on ANOVA Every Day Benefits of a Feel for Statistics and for Evaluating Data.
 (source: Nielsen Book Data)9780803970755 20160618
(source: Nielsen Book Data)9780803970755 20160618
 Introduction The Need for Analysis of Variance (ANOVA) Means, Variances, Sums of Squares and Degrees of Freedom Independent Group ANOVAs OneFactor Independent Groups ANOVA Multiple Comparisons: Independent Groups tTests TwoFactor Independent Groups ANOVA Repeated Measures ANOVAs OneFactor Repeated Measures ANOVA Multiple Comparisons: Dependent Measures tTests TwoFactor Mixed Measures ANOVA TwoFactor Repeated Measures ANOVA Overview and Final Thoughts Some Tips for Tests on ANOVA Every Day Benefits of a Feel for Statistics and for Evaluating Data.
 (source: Nielsen Book Data)9780803970755 20160618
(source: Nielsen Book Data)9780803970755 20160618
 Book
 xix, 204 p. : ill. ; 23 cm.
 The Study of Variation. OneWay Classification. TwoWay CrossClassification. Randomized Blocks. BIBDs and Latin Squares. Nested Classifications. Maximum Likelihood Estimation. The MINQUE and MIVQUE. NonNegative Estimation of Variance Components. Confidence Intervals. Genetic and Environmental Effects.
 (source: Nielsen Book Data)9780412728600 20160528
(source: Nielsen Book Data)9780412728600 20160528
 The Study of Variation. OneWay Classification. TwoWay CrossClassification. Randomized Blocks. BIBDs and Latin Squares. Nested Classifications. Maximum Likelihood Estimation. The MINQUE and MIVQUE. NonNegative Estimation of Variance Components. Confidence Intervals. Genetic and Environmental Effects.
 (source: Nielsen Book Data)9780412728600 20160528
(source: Nielsen Book Data)9780412728600 20160528
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QA279 .R363 1997  Available 
11. An introduction to the analysis of variance [1994]
 Book
 565 p.
Covers topics in the analysis of variance, such as: the nature of interaction and its interpretation, in terms of theory and response scale transformations; nonorthogonal designs; generalized forms of analysis of covariance; and discussion of functional measurement.
(source: Nielsen Book Data)9780275947200 20160528
(source: Nielsen Book Data)9780275947200 20160528
Covers topics in the analysis of variance, such as: the nature of interaction and its interpretation, in terms of theory and response scale transformations; nonorthogonal designs; generalized forms of analysis of covariance; and discussion of functional measurement.
(source: Nielsen Book Data)9780275947200 20160528
(source: Nielsen Book Data)9780275947200 20160528
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QA279 .B64 1994  Available 
12. Elements of generalizability theory [1992]
 Book
 xiv, 161 p. : ill. ; 28 cm.
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BF39 .B74 1992  Available 
13. Variance components [1992]
 Book
 xxiii, 501 p. ; 25 cm.
 History and comment the 1way classification balanced data analysis of variance estimation for unbalanced data maximum likelihood (ML) and restricted maximum likelihood (REML) prediction of random variables computing ML and REML estimates hierarchical models and Bayesian estimation binary and discrete data other procedures the dispersionmean model.
 (source: Nielsen Book Data)9780471621621 20160527
(source: Nielsen Book Data)9780471621621 20160527
 History and comment the 1way classification balanced data analysis of variance estimation for unbalanced data maximum likelihood (ML) and restricted maximum likelihood (REML) prediction of random variables computing ML and REML estimates hierarchical models and Bayesian estimation binary and discrete data other procedures the dispersionmean model.
 (source: Nielsen Book Data)9780471621621 20160527
(source: Nielsen Book Data)9780471621621 20160527
dx.doi.org Wiley Online Library
 dx.doi.org Wiley Online Library
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Marine Biology Library (Miller), Science Library (Li and Ma)
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QA279 .S428 1992  Unknown 
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 Book
 430 p.
 Concepts and Examples in Analysis of Variance (J. Tukey, et al.) Purposes of Analyzing Data that Come in a Form Inviting Us to Apply Tools from the Analysis of Variance (F. Mosteller & J. Tukey) Preliminary Examination of Data (F. Mosteller & D. Hoaglin) Types of Factors and Their Structural Layouts (J. Singer) ValueSplitting: Taking the Data Apart (C. Schmid) ValueSplitting Involving More Factors (K. Halvorsen) Mean Squares, F Tests, and Estimates of Variance (F. Mosteller, et al.) Graphical Display as an Aid to Analysis (J. Emerson) Components of Variance (C. Brown & F. Mosteller) Which Denominator? (T. Blackwell, et al.) Assessing Changes (J. Tukey, et al.) Qualitative and Quantitative Confidence (J. Tukey & D. Hoaglin) Introduction to Transformation (J. Emerson) Appendix Index.
 (source: Nielsen Book Data)9780471527350 20160528
(source: Nielsen Book Data)9780471527350 20160528
 Concepts and Examples in Analysis of Variance (J. Tukey, et al.) Purposes of Analyzing Data that Come in a Form Inviting Us to Apply Tools from the Analysis of Variance (F. Mosteller & J. Tukey) Preliminary Examination of Data (F. Mosteller & D. Hoaglin) Types of Factors and Their Structural Layouts (J. Singer) ValueSplitting: Taking the Data Apart (C. Schmid) ValueSplitting Involving More Factors (K. Halvorsen) Mean Squares, F Tests, and Estimates of Variance (F. Mosteller, et al.) Graphical Display as an Aid to Analysis (J. Emerson) Components of Variance (C. Brown & F. Mosteller) Which Denominator? (T. Blackwell, et al.) Assessing Changes (J. Tukey, et al.) Qualitative and Quantitative Confidence (J. Tukey & D. Hoaglin) Introduction to Transformation (J. Emerson) Appendix Index.
 (source: Nielsen Book Data)9780471527350 20160528
(source: Nielsen Book Data)9780471527350 20160528
dx.doi.org Wiley Online Library
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 x, 101 p. : ill. ; 28 cm.
Green Library, Earth Sciences Library (Branner)
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 Book
 vi, 152 p. : ill. ; 28 cm.
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17. Analysis of variance : the basic designs [1986]
 Book
 x, 310 p. : ill. ; 25 cm.
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BF39 .C578 1986  Available 
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 x, 144 p. : ill. ; 24 cm.
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 p. 687699 ; 23 cm.
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20. Analysis of variance [1980]
 Book
 xvii, 1002 p. : ill. ; 25 cm.
 Estimation of Variance Components (C.R. Rao, J. Kleffe). Multivariate Analysis of Variance of Repeated Measurements (N.H. Timm). Growth Curve Analysis (S. Geisser). Bayesian Inference in MANOVA (S.J. Press). Graphical Methods for Internal Comparisons in ANOVA and MANOVA (R. Gnanadesikan). Monotonicity and Unbiasedness Properties of ANOVA and MANOVA Tests (S.D. Gupta). Robustness of ANOVA and MANOVA Test Procedures (P.K. Ito). Analysis of Variance and Problems under Time Series Models (D.R. Brillinger). Tests of Univariate and Multivariate Normality (K.V. Mardia). Transformations to Normality (G. Kaskey et al.). ANOVA and MANOVA: Models for Categorical Data (V.P. Bhapkar). Inference and the Structural Model for ANOVA and MANOVA (D.A.S. Fraser). Inference Based on Conditionally Specified ANOVA Models Incorporating Preliminary Testing (T.A. Bancroft, C.P. Han). Quadratic Forms in Normal Variables (C.G. Khatri). Generalized Inverse of Matrices and Applications to Linear Models (S.K. Mitra). Likelihood Ratio Tests for Mean Vectors and Covariance Matrices (P.R. Krishnaiah, J.C. Lee). Assessing Dimensionality in Multivariate Regression (A.J. Izenman). Parameter Estimation in Nonlinear Regression Models (H. Bunke). Early History of Multiple Comparison Tests (H.L. Harter). Representations of Simultaneous Pairwise Comparisons (A.R. Sampson). Simultaneous Test Procedures for Mean Vectors and Covariance Matrices (P.R. Krishnaiah, G.S. Mudholkar, P. Subbaiah). Nonparametric Simultaneous Inference for Some MANOVA Models (P.K. Sen). Comparison of Some Computer Programs for Univariate and Multivariate Analysis of Variance (R.D. Bock, D. Brandt). Computations of Some Multivariate Distributions (P.R. Krishnaiah). Inference on the Structure of Interaction in a TwoWay Classification Model (P.R. Krishnaiah, M.G. Yochmowitz). Index.
 (source: Nielsen Book Data)9780444853356 20160528
(source: Nielsen Book Data)9780444853356 20160528
 Estimation of Variance Components (C.R. Rao, J. Kleffe). Multivariate Analysis of Variance of Repeated Measurements (N.H. Timm). Growth Curve Analysis (S. Geisser). Bayesian Inference in MANOVA (S.J. Press). Graphical Methods for Internal Comparisons in ANOVA and MANOVA (R. Gnanadesikan). Monotonicity and Unbiasedness Properties of ANOVA and MANOVA Tests (S.D. Gupta). Robustness of ANOVA and MANOVA Test Procedures (P.K. Ito). Analysis of Variance and Problems under Time Series Models (D.R. Brillinger). Tests of Univariate and Multivariate Normality (K.V. Mardia). Transformations to Normality (G. Kaskey et al.). ANOVA and MANOVA: Models for Categorical Data (V.P. Bhapkar). Inference and the Structural Model for ANOVA and MANOVA (D.A.S. Fraser). Inference Based on Conditionally Specified ANOVA Models Incorporating Preliminary Testing (T.A. Bancroft, C.P. Han). Quadratic Forms in Normal Variables (C.G. Khatri). Generalized Inverse of Matrices and Applications to Linear Models (S.K. Mitra). Likelihood Ratio Tests for Mean Vectors and Covariance Matrices (P.R. Krishnaiah, J.C. Lee). Assessing Dimensionality in Multivariate Regression (A.J. Izenman). Parameter Estimation in Nonlinear Regression Models (H. Bunke). Early History of Multiple Comparison Tests (H.L. Harter). Representations of Simultaneous Pairwise Comparisons (A.R. Sampson). Simultaneous Test Procedures for Mean Vectors and Covariance Matrices (P.R. Krishnaiah, G.S. Mudholkar, P. Subbaiah). Nonparametric Simultaneous Inference for Some MANOVA Models (P.K. Sen). Comparison of Some Computer Programs for Univariate and Multivariate Analysis of Variance (R.D. Bock, D. Brandt). Computations of Some Multivariate Distributions (P.R. Krishnaiah). Inference on the Structure of Interaction in a TwoWay Classification Model (P.R. Krishnaiah, M.G. Yochmowitz). Index.
 (source: Nielsen Book Data)9780444853356 20160528
(source: Nielsen Book Data)9780444853356 20160528
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