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Linear models [[electronic resource] ] : the theory and application of analysis of variance / / Brenton R. Clarke
| Linear models [[electronic resource] ] : the theory and application of analysis of variance / / Brenton R. Clarke |
| Autore | Clarke Brenton R |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2008 |
| Descrizione fisica | 1 online resource (xviii, 241 p.) : illustrated |
| Disciplina | 519.5/38 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Linear models (Statistics)
Analysis of variance |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-76672-0
9786611766726 0-470-37799-2 0-470-37797-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Projection matrices and vector space theory -- Least squares theory -- Distribution theory -- Helmert matrices and orthogonal relationships -- Further discussion of ANOVA -- Residual analysis: diagnostics and robustness -- Models that include variance components -- Likelihood approaches -- Uncorrelated residuals formed from the linear model -- Further inferential questions relating to ANOVA. |
| Record Nr. | UNINA-9910144102803321 |
Clarke Brenton R
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| Hoboken, N.J., : Wiley, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Linear models [[electronic resource] ] : the theory and application of analysis of variance / / Brenton R. Clarke
| Linear models [[electronic resource] ] : the theory and application of analysis of variance / / Brenton R. Clarke |
| Autore | Clarke Brenton R |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2008 |
| Descrizione fisica | 1 online resource (xviii, 241 p.) : illustrated |
| Disciplina | 519.5/38 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Linear models (Statistics)
Analysis of variance |
| ISBN |
1-281-76672-0
9786611766726 0-470-37799-2 0-470-37797-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Projection matrices and vector space theory -- Least squares theory -- Distribution theory -- Helmert matrices and orthogonal relationships -- Further discussion of ANOVA -- Residual analysis: diagnostics and robustness -- Models that include variance components -- Likelihood approaches -- Uncorrelated residuals formed from the linear model -- Further inferential questions relating to ANOVA. |
| Record Nr. | UNINA-9910830304503321 |
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Clarke Brenton R
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||
| Hoboken, N.J., : Wiley, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Linear models [[electronic resource] ] : the theory and application of analysis of variance / / Brenton R. Clarke
| Linear models [[electronic resource] ] : the theory and application of analysis of variance / / Brenton R. Clarke |
| Autore | Clarke Brenton R |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2008 |
| Descrizione fisica | 1 online resource (xviii, 241 p.) : illustrated |
| Disciplina | 519.5/38 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Linear models (Statistics)
Analysis of variance |
| ISBN |
1-281-76672-0
9786611766726 0-470-37799-2 0-470-37797-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Projection matrices and vector space theory -- Least squares theory -- Distribution theory -- Helmert matrices and orthogonal relationships -- Further discussion of ANOVA -- Residual analysis: diagnostics and robustness -- Models that include variance components -- Likelihood approaches -- Uncorrelated residuals formed from the linear model -- Further inferential questions relating to ANOVA. |
| Record Nr. | UNINA-9910840713403321 |
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Clarke Brenton R
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| Hoboken, N.J., : Wiley, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Local variance estimation for uncensored and censored observations / / Paola Gloria Ferrario
| Local variance estimation for uncensored and censored observations / / Paola Gloria Ferrario |
| Autore | Ferrario Paola Gloria |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Weisbaden, Germany : , : Springer Vieweg, , 2013 |
| Descrizione fisica | 1 online resource (xvii, 130 pages) : illustrations |
| Disciplina | 519.546 |
| Collana | Gale eBooks |
| Soggetto topico |
Analysis of variance
Estimation theory Censored observations (Statistics) |
| ISBN | 3-658-02314-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Least Squares Estimation of the Local Variance via Plug-In.- Local Averaging Estimation of the Local Variance via Plug-In -- Partitioning Estimation of the Local Variance via Nearest Neighbors -- Estimation of the Local Variance under Censored Observations. |
| Record Nr. | UNINA-9910437860803321 |
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Ferrario Paola Gloria
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| Weisbaden, Germany : , : Springer Vieweg, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematical Portfolio Theory and Analysis / / Siddhartha Pratim Chakrabarty, Ankur Kanaujiya
| Mathematical Portfolio Theory and Analysis / / Siddhartha Pratim Chakrabarty, Ankur Kanaujiya |
| Autore | Chakrabarty Siddhartha Pratim |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Birkhäuser, , [2023] |
| Descrizione fisica | 1 online resource (158 pages) |
| Disciplina | 519.538 |
| Collana | Compact Textbooks in Mathematics Series |
| Soggetto topico |
Analysis of variance
Portfolio management - Mathematical models |
| ISBN | 981-19-8544-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Mechanisms of Financial Markets -- Chapter 2. Fundamentals of Probability Theory -- Chapter 3. Asset Pricing Models -- Chapter 4. Mean-Variance Portfolio Theory -- Chapter 5. Utility Theory -- Chapter 6. Non-Mean-Variance Portfolio Theory -- Chapter 7. Optimal Portfolio Strategies -- Chapter 8. Bond Portfolio Optimization -- Chapter 9. Risk Management of Portfolios. |
| Record Nr. | UNINA-9910672447803321 |
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Chakrabarty Siddhartha Pratim
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| Singapore : , : Birkhäuser, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Methods and applications of linear models : regression and the analysis of variance / / Ronald R. Hocking, PenHock Statistical Consultants
| Methods and applications of linear models : regression and the analysis of variance / / Ronald R. Hocking, PenHock Statistical Consultants |
| Autore | Hocking R. R (Ronald R.), <1932-> |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2013 |
| Descrizione fisica | 1 online resource (717 p.) |
| Disciplina | 519.5/36 |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Regression analysis
Analysis of variance Linear models (Statistics) MATHEMATICS / Probability & Statistics / General |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-118-59302-2
1-118-64019-5 1-118-59304-9 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Methods and Applications of Linear Models; Contents; Preface to the Third Edition; Preface to the Second Edition; Preface to the First Edition; PART I REGRESSION; 1 Introduction to Linear Models; 1.1 Background Information; 1.2 Mathematical and Statistical Models; 1.3 Definition of the Linear Model; 1.4 Examples of Regression Models; 1.4.1 Single-Variable, Regression Model; 1.4.2 Regression Models with Several Inputs; 1.4.3 Discrete Response Variables; 1.4.4 Multivariate Linear Models; 1.5 Concluding Comments; Exercises; 2 Regression on Functions of One Variable
2.1 The Simple Linear Regression Model2.2 Parameter Estimation; 2.2.1 Least Squares Estimation; 2.2.2 Maximum Likelihood Estimation; 2.2.3 Coded Data: Centering and Scaling; 2.2.4 The Analysis of Variance Table; 2.3 Properties of the Estimators and Test Statistics; 2.3.1 Moments of Linear Functions of Random Variables; 2.3.2 Moments of Least Squares Estimators; 2.3.3 Distribution of the Least Squares Estimators; 2.3.4 The Distribution of Test Statistics; 2.4 The Analysis of Simple Linear Regression Models; 2.4.1 Two Numerical Examples; 2.4.2 A Test for Lack-of-Fit 2.4.3 Inference on the Parameters of the Model2.4.4 Prediction and Prediction Intervals; 2.5 Examining the Data and the Model; 2.5.1 Residuals; 2.5.2 Outliers, Extreme Points, and Influence; 2.5.3 Normality, Independence, and Variance Homogeneity; 2.6 Polynomial Regression Models; 2.6.1 The Quadratic Model; 2.6.2 Higher Ordered Polynomial Models; 2.6.3 Orthogonal Polynomials; 2.6.4 Regression through the Origin; Exercises; 3 Transforming the Data; 3.1 The Need for Transformations; 3.2 Weighted Least Squares; 3.3 Variance Stabilizing Transformations 3.4 Transformations to Achieve a Linear Model3.4.1 Transforming the Dependent Variable; 3.4.2 Transforming the Predictors; 3.5 Analysis of the Transformed Model; 3.5.1 Transformations with Forbes Data; Exercises; 4 Regression on Functions of Several Variables; 4.1 The Multiple Linear Regression Model; 4.2 Preliminary Data Analysis; 4.3 Analysis of the Multiple Linear Regression Model; 4.3.1 Fitting the Model in Centered Form; 4.3.2 Estimation and Analysis of the Original Data; 4.3.3 Model Assessment and Residual Analysis; 4.3.4 Prediction; 4.3.5 Transforming the Response 4.4 Partial Correlation and Added-Variable Plots4.4.1 Partial Correlation; 4.4.2 Added-Variable Plots; 4.4.3 Simple Versus Partial Correlation; 4.5 Variable Selection; 4.5.1 The Case of Orthogonal Predictors; 4.5.2 Criteria for Deletion of Variables; 4.5.3 Nonorthogonal Predictors; 4.5.4 Computational Considerations; 4.5.5 Selection Strategies; 4.6 Model Specification; 4.6.1 Application to Subset Selection; 4.6.2 Improved Mean Squared Error; 4.6.3 Development of the Cp Statistic; Exercises; 5 Collinearity in Multiple Linear Regression; 5.1 The Collinearity Problem; 5.1.1 Introduction 5.1.2 A Simple Example |
| Record Nr. | UNINA-9910452282203321 |
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Hocking R. R (Ronald R.), <1932->
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||
| Hoboken, New Jersey : , : John Wiley & Sons, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Methods and applications of linear models [[electronic resource] ] : regression and the analysis of variance / / Ronald R. Hocking
| Methods and applications of linear models [[electronic resource] ] : regression and the analysis of variance / / Ronald R. Hocking |
| Autore | Hocking R. R (Ronald R.), <1932-> |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2013 |
| Descrizione fisica | xxiii, 681 p. : ill |
| Disciplina | 519.5/36 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Regression analysis
Analysis of variance Linear models (Statistics) |
| ISBN |
1-118-59304-9
1-118-59302-2 1-118-64019-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Regression -- pt. 2. The analysis of variance. |
| Record Nr. | UNINA-9910795806403321 |
|
Hocking R. R (Ronald R.), <1932->
|
||
| Hoboken, N.J., : Wiley, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Methods and applications of linear models [[electronic resource] ] : regression and the analysis of variance / / Ronald R. Hocking
| Methods and applications of linear models [[electronic resource] ] : regression and the analysis of variance / / Ronald R. Hocking |
| Autore | Hocking R. R (Ronald R.), <1932-> |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2013 |
| Descrizione fisica | xxiii, 681 p. : ill |
| Disciplina | 519.5/36 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Regression analysis
Analysis of variance Linear models (Statistics) |
| ISBN |
1-118-59304-9
1-118-59302-2 1-118-64019-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Regression -- pt. 2. The analysis of variance. |
| Record Nr. | UNINA-9910820135803321 |
|
Hocking R. R (Ronald R.), <1932->
|
||
| Hoboken, N.J., : Wiley, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Methods and applications of linear models [[electronic resource] ] : regression and the analysis of variance / / Ronald R. Hocking
| Methods and applications of linear models [[electronic resource] ] : regression and the analysis of variance / / Ronald R. Hocking |
| Autore | Hocking R. R (Ronald R.), <1932-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2003 |
| Descrizione fisica | 1 online resource (773 pages) |
| Disciplina | 519.536 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Regression analysis
Analysis of variance Linear models (Statistics) |
| ISBN |
1-280-27269-4
9786610272693 0-470-30647-5 0-471-45862-7 0-471-43415-9 0-471-23222-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Methods and Applications of Linear Models -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- Part 1 Regression Models -- 1 Introduction to Linear Models -- 1.1 Background Information -- 1.2 Mathematical and Statistical Models -- 1.3 Definition of the Linear Model -- 1.4 Examples of Regression Models -- 1.4.1 Single variable, Regression Model -- 1.4.2 Regression Models with Several Inputs -- 1.4.3 Discrete Response Variables -- 1.4.4 Multivariate Linear Models -- 1.5 Concluding Comments -- Exercises -- 2 Regression on Functions of One Variable -- 2.1 Simple Linear Regression Model -- 2.2 Parameter Estimation -- 2.2.1 Least Squares Estimation -- 2.2.2 Maximum Likelihood Estimation -- 2.2.3 Coded Data: Centering and Scaling -- 2.3 Properties of the Estimators -- 2.4 Analysis of the Simple Linear Regression Model -- 2.4.1 Parameter Estimation -- 2.4.2 Inference on the Parameters of the Model -- 2.4.3 Prediction and Prediction Intervals -- 2.5 Examining the Data and the Model -- 2.5.1 Residuals -- 2.5.2 Outliers, Extreme Points, and Influence -- 2.5.3 Normality, Independence and Variance Homogeneity -- 2.6 Test for Lack of Fit -- 2.7 Polynomial Regression Models -- 2.7.1 Quadratic Model -- 2.7.2 Higher-Ordered Polynomial Models -- 2.7.3 Orthogonal Polynomials -- 2.7.4 Regression Through the Origin -- Exercises -- 3 Transforming the Data -- 3.1 Need for Transformations -- 3.2 Weighted Least Squares -- 3.3 Variance Stabilizing Transformations -- 3.4 Transformations to Achieve a Linear Model -- 3.4.1 Transforming the Dependent Variable -- 3.4.2 Transforming the Predictors -- 3.5 Analysis of the Transformed Model -- 3.6 Transformations with Forbes Data -- Exercises -- 4 Regression on Functions of Several Variables -- 4.1 Multiple Linear Regression Model -- 4.2 Preliminary Data Analysis.
4.3 Analysis of the Multiple Linear Regression Model -- 4.3.1 Fitting the Model in Centered Form -- 4.3.2 Estimation and Analysis of the Original Data -- 4.3.3 Model Assessment and Residual Analysis -- 4.3.4 Prediction -- 4.3.5 Transforming the Response -- 4.4 Partial Correlation and Added-Variable Plots -- 4.4.1 Partial Correlation -- 4.4.2 Added-Variable Plots -- 4.4.3 Simple Versus Partial Correlation -- 4.5 Variable Selection -- 4.5.1 Orthogonal Predictors -- 4.5.2 Criteria for Deletion of Variables -- 4.5.3 Non-Orthogonal Predictors -- 4.5.4 Computational Considerations -- 4.5.5 Selection Strategies -- 4.6 Model Specification -- Exercises -- 5 Collinearity in Multiple Linear Regression -- 5.1 Collinearity Problem -- 5.1.1 Introduction -- 5.1.2 Simple Example -- 5.1.3 The Picket Fence -- 5.1.4 Rotation of Coordinates -- 5.2 Example With Collinearity -- 5.2.1 Preliminary Data Analysis -- 5.2.2 Initial Regression Analysis -- 5.3 Collinearity Diagnostics -- 5.3.1 Variance Inflation Factors -- 5.3.2 Eigenvalues, Eigenvectors and Principal Component Plots -- 5.4 Remedial Solutions: Biased Estimators -- 5.4.1 Variable Deletion -- 5.4.2 Regression on Principal Components -- 5.4.3 Ridge Regression -- Exercises -- 6 Influential Observations in Multiple Linear Regression -- 6.1 Influential Data Problem -- 6.2 Hat Matrix -- 6.2.1 Centered and Uncentered Hat Matrices -- 6.2.2 Properties of the Hat Matrices -- 6.3 Effects of Deleting Observations -- 6.4 Numerical Measures of Influence -- 6.4.1 Diagonal Elements of the Hat Matrix -- 6.4.2 Residuals -- 6.4.3 Mean Square Ratio -- 6.4.4 Cook's Distance -- 6.4.5 Other Indicators of Influential Data -- 6.5 Dilemma Data -- 6.6 Plots for Identifying Unusual Cases -- 6.6.1 Projection Ellipse -- 6.6.2 Augmented Hat Matrix -- 6.6.3 Multiple Extremes: The Masking Problem. 6.7 Robust/Resistant Methods in Regression Analysis -- 6.7.1 M-Estimation -- 6.7.2 Iterative, Reweighted Least Squares -- 6.7.3 Regression with Bounded Influence -- Exercises -- 7 Polynomial Models and Qualitative Predictors -- 7.1 Polynomial Models -- 7.1.1 Quadratic Model with Two Predictors -- 7.1.2 Quadratic Surfaces -- 7.2 Analysis of Response Surfaces -- 7.2.1 Analysis with First-Order Models -- 7.2.2 Analysis with Second-Order Models -- 7.3 Models with Qualitative Predictors -- 7.3.1 Indicator Variables to Identify Groups of Data -- 7.3.2 Indicator Variables to Fit Segmented Polynomials -- Exercises -- 8 Additional Topics -- 8.1 Non-Linear Regression Models -- 8.1.1 Some Linearizeable Functions -- 8.1.2 Modified Gauss-Newton Method -- 8.2 Non-Parametric Model-Fitting Methods -- 8.2.1 Locally Weighted-Average Predictors -- 8.2.2 Projection Pursuit Regression -- 8.3 Logistic Regression -- 8.4 Random Input Variables -- 8.5 Errors in the Inputs -- 8.6 Calibration -- Exercises -- Part II Analysis of Variance Models -- 9 Introduction to Analysis of Variance Models -- 9.1 Background Information -- 9.2 Cell Means Model -- 9.3 Fixed Effects Models -- 9.3.1 One-way Classification Model -- 9.3.2 Two-way Classification Model -- 9.3.3 Constrained Cell Means Model -- 9.4 Mixed Effects Models -- 9.5 Concluding Comments -- Exercises -- 10 Fixed Effects Models I: One-way Classification of Means -- 10.1 Introduction -- 10.2 One- Way Classification: Balanced Data -- 10.2.1 Parameter Estimation -- 10.2.2 Hypothesis of Equal Means -- 10.2.3 Simultaneous Inferences About the Population Means -- 10.2.4 Simultaneous Acceptance and Confidence Ellipses -- 10.2.5 Orthogonal Contrasts -- 10.2.6 Reparameterizations of the One-way Model -- 10.3 One- Way Classification: Unbalanced Data -- 10.4 Analysis of Covariance -- Exercises. 11 Fixed Effects Models II: Two-way Classification of Means -- 11.1 Unconstrained Model: Balanced Data -- 11.1.1 Parameter Estimation -- 11.1.2 Tests of Hypotheses -- 11.1.3 Simultaneous Inference -- 11.1.4 Reparameterizations of the Two-Factor Model -- 11 . 1.5 Test for Interaction with One Observation per Cell -- 11.2 Unconstrained Model: Unbalanced Data -- 11.2.1 Discussion in Terms of the Cell Means Model -- 11.2.2 Reparameterized Model -- 11.3 No-Interaction Model: Balanced Data -- 11.3.1 Parameter Estimation -- 11.3.2 Tests of Hypotheses -- 11.3.3 Simultaneous Inference -- 11.3.4 Reparameterization of the No-Interaction Model -- 11.4 No-Interaction Model: Unbalanced Data -- 11.4.1 Missing Cells: Estimation -- 11.4.2 Missing Cells: Testing Hypotheses -- 11.4.3 Connected Designs -- 11.5 Non-Homogeneous Experimental Units: The Concept of Blocking -- 11.5.1 Model for the Randomized, Complete Block Design -- 11.5.2 Inferences on Parameters -- Exercises -- 12 Fixed Effects Models III: Multiple Crossed and Nested Factors -- 12.1 Three-Factor Cross-Classified Model -- 12.1.1 Tests of Hypotheses -- 12.1.2 Reparameterized Model -- 12.1.3 Estimability and Testability with Missing Cells -- 12.2 General Structure for Balanced, Factorial Models -- 12.3 Two-Fold Nested Model -- 12.3.1 Analysis with Balanced Data -- 12.3.2 Analysis with Unbalanced Data -- 12.4 General Structure for Balanced, Nested Models -- 12.5 Three-Factor, Nested-Factorial Model -- 12.5.1 Analysis with Balanced Data -- 12.5.2 Analysis with Unbalanced Data -- 12.6 General Structure for Balanced, Nested-Factorial Models -- Exercises -- 13 Mixed Models I: The AOV Method with Balanced Data -- 13.1 Introduction -- 13.2 One-way Classitication, Random Model -- 13.3 Two-way Classification, Mixed Model -- 13.4 Three-Factor, Nested-Factorial Model -- 13.5 General Analysis for Balanced, Mixed Models. 13.5.1 Description of the Model -- 13.5.2 Parameter Estimation -- 13.5.3 Properties of the Estimators and Inferential Results -- 13.6 Additional Examples -- 13.6.1 Two-Fold Nested, Random Model -- 13.6.2 Randomized Block Design -- 13.6.3 Mixed Model for Split-Plot Designs -- 13.6.4 Repeated Measures Designs -- 13.6.5 Longitudinal Studies -- 13.7 Alternative Development of Mixed Models -- 13.7.1 Graybill Mixed Model -- 13.7.2 Scheffé Mixed Model -- 13.7.3 Randomization Theory -- Exercises -- 14 Mixed Models II: The AVE Method with Balanced Data -- 14.1 Introduction -- 14.2 Two-way Cross-Classification Model -- 14.3 Two-Fold Nested Model -- 14.4 Three Factor, Nested-Factorial Model -- 14.5 General Description of the AVE Table -- 14.5.1 AVE Table for Factorial Models -- 14.5.2 AVE Table for Nested Models -- 14.5.3 AVE Table for Nested-Factorial Models -- 14.5.4 AVE Method for General Mixed Effects Models -- 14.6 Additional Examples -- 14.7 Computational Procedure for the AVE Method -- 14.8 Properties of the AVE Estimates -- 14.8.1 Diagnostic Analysis for the Two-way Classification Model -- 14.8.2 Confidence Intervals -- Exercises -- 15 Mixed Models III: Unbalanced Data -- 15.1 Introduction -- 15.2 Parameter Estimation: Likelihood Methods -- 15.2.1 Maximum Likelihood Estimation -- 15.2.2 Restricted Maximum Likelihood Estimation -- 15.2.3 Minimum Norm Quadratic Unbiased Estimators -- 15.2.4 Numerical Illustration of the Methods -- 15.3 ML and REML, Estimates with Balanced Data -- 15.3.1 ML Estimation with Balanced Data -- 15.3.2 REML, Estimates with Balanced Data -- 15.4 EM Algorithm for REML Estimation -- 15.4.1 Review of the EM Algorithm -- 15.4.2 EM Algorithm Applied to REML, Estimation -- 15.4.3 Estimation of Fixed Effects -- 15.4.4 Inferences on Variance Components and Fixed Effects -- 15.4.5 Numerical Examples to Illustrate the EM-AOV Algorithm. 15.5 EM Algorithm Applied to the AVE Method. |
| Record Nr. | UNINA-9910146081903321 |
|
Hocking R. R (Ronald R.), <1932->
|
||
| Hoboken, N.J., : Wiley-Interscience, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Methods and applications of linear models : regression and the analysis of variance / Ronald R. Hocking
| Methods and applications of linear models : regression and the analysis of variance / Ronald R. Hocking |
| Autore | Hocking, Ronald R. |
| Pubbl/distr/stampa | New York : J. Wiley & Sons, c1996 |
| Descrizione fisica | xxii, 731 p. : ill. ; 24 cm. |
| Disciplina | 519.536 |
| Collana | Wiley series in probability and statistics. Applied probability and statistics |
| Soggetto topico |
Analysis of variance
Linear models (Statistics) Regression analysis |
| ISBN | 047159282X |
| Classificazione |
AMS 62J
QA278.2.H63 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991001137599707536 |
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Hocking, Ronald R.
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| New York : J. Wiley & Sons, c1996 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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