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Regression graphics [[electronic resource] ] : ideas for studying regressions through graphics / / R. Dennis Cook
Regression graphics [[electronic resource] ] : ideas for studying regressions through graphics / / R. Dennis Cook
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, c1998
Descrizione fisica 1 online resource (378 p.)
Disciplina 519.536
519.536028
Collana Wiley series in probability and statistics Probability and statistics section
Soggetto topico Multivariate analysis
Regression analysis - Graphic methods
ISBN 1-282-30757-6
9786612307577
0-470-31693-4
0-470-31777-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Regression Graphics Ideas for Studying Regressions through Graphics; Contents; Preface; 1. Introduction; 1.1. C.C & I,1; 1.1.1. Construction; 1.1.3. Inference; 1.1.2. Characterization; 1.2. Illustrations; 1.2.1. Residuals versus fitted values; 1.2.2. Residuals versus the predictors; 1.2.3. Residuals versus the response; 1.3. On things to come; 1.4. Notational conventions; Problems; 2. Introduction to 2D Scatterplots; 2.1. Response plots in simple regression; 2.2. New Zealand horse mussels; 2.3. Transforming y via inverse response plots; 2.3.1 Response transformations
2.3.2 Response transformations: Mussel data2.4. Danish twins; 2.5. Scatterplot matrices; 2.5.1 Consrruction; 2.5.2 Example; 2.6. Regression graphics in the 1920s; 2.6.1. Ezekiel's successive approximations; 2.6.2. Bean's graphic method; 2.7. Discussion; Problems; 3. Constructing 3D Scatterplots; 3.1. Getting an impression of 3D; 3.2. Depth cuing; 3.3. Scaling; 3.4. Orthogonalization; Problems; 4. Interpreting 3D Scatterplots; 4.1. Haystacks; 4.2. Structural dimensionality; 4.2.1. One predictor; 4.2.2. Two predictors; 4.2.3 Many predictors; 4.3. One-dimensional structure
4.4. Two-dimensional structure4.4.1. Removing linear trends; 4.4.2. Identifying semiparametric regression functions; 4.5. Assessing structural dimensionality; 4.5.1. A visual metaphor for structural dimension; 4.5.2. A first method for deciding d = 1 or 2; 4.5.3. Natural rubber; 4.6. Assessment methods; 4.6.1. Using independence; 4.6.2. Using uncorrelated 2D views; 4.6.3. Uncorrelated 2D views: Haystack data; 4.6.4. Intraslice residuals; 4.6.5. Intraslice orthogonalization; 4.6.6. Mussels again; 4.6.7. Discussion; Problems; 5. Binary Response Variables; 5.1. One predictor; 5.2. Two predictors
7.5.2 Conditions for S ylx1=S(n1)
Record Nr. UNINA-9910144684803321
Cook R. Dennis  
New York, : Wiley, c1998
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Regression graphics [[electronic resource] ] : ideas for studying regressions through graphics / / R. Dennis Cook
Regression graphics [[electronic resource] ] : ideas for studying regressions through graphics / / R. Dennis Cook
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, c1998
Descrizione fisica 1 online resource (378 p.)
Disciplina 519.536
519.536028
Collana Wiley series in probability and statistics Probability and statistics section
Soggetto topico Multivariate analysis
Regression analysis - Graphic methods
ISBN 1-282-30757-6
9786612307577
0-470-31693-4
0-470-31777-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Regression Graphics Ideas for Studying Regressions through Graphics; Contents; Preface; 1. Introduction; 1.1. C.C & I,1; 1.1.1. Construction; 1.1.3. Inference; 1.1.2. Characterization; 1.2. Illustrations; 1.2.1. Residuals versus fitted values; 1.2.2. Residuals versus the predictors; 1.2.3. Residuals versus the response; 1.3. On things to come; 1.4. Notational conventions; Problems; 2. Introduction to 2D Scatterplots; 2.1. Response plots in simple regression; 2.2. New Zealand horse mussels; 2.3. Transforming y via inverse response plots; 2.3.1 Response transformations
2.3.2 Response transformations: Mussel data2.4. Danish twins; 2.5. Scatterplot matrices; 2.5.1 Consrruction; 2.5.2 Example; 2.6. Regression graphics in the 1920s; 2.6.1. Ezekiel's successive approximations; 2.6.2. Bean's graphic method; 2.7. Discussion; Problems; 3. Constructing 3D Scatterplots; 3.1. Getting an impression of 3D; 3.2. Depth cuing; 3.3. Scaling; 3.4. Orthogonalization; Problems; 4. Interpreting 3D Scatterplots; 4.1. Haystacks; 4.2. Structural dimensionality; 4.2.1. One predictor; 4.2.2. Two predictors; 4.2.3 Many predictors; 4.3. One-dimensional structure
4.4. Two-dimensional structure4.4.1. Removing linear trends; 4.4.2. Identifying semiparametric regression functions; 4.5. Assessing structural dimensionality; 4.5.1. A visual metaphor for structural dimension; 4.5.2. A first method for deciding d = 1 or 2; 4.5.3. Natural rubber; 4.6. Assessment methods; 4.6.1. Using independence; 4.6.2. Using uncorrelated 2D views; 4.6.3. Uncorrelated 2D views: Haystack data; 4.6.4. Intraslice residuals; 4.6.5. Intraslice orthogonalization; 4.6.6. Mussels again; 4.6.7. Discussion; Problems; 5. Binary Response Variables; 5.1. One predictor; 5.2. Two predictors
7.5.2 Conditions for S ylx1=S(n1)
Record Nr. UNINA-9910829913403321
Cook R. Dennis  
New York, : Wiley, c1998
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Regression graphics [[electronic resource] ] : ideas for studying regressions through graphics / / R. Dennis Cook
Regression graphics [[electronic resource] ] : ideas for studying regressions through graphics / / R. Dennis Cook
Autore Cook R. Dennis
Pubbl/distr/stampa New York, : Wiley, c1998
Descrizione fisica 1 online resource (378 p.)
Disciplina 519.536
519.536028
Collana Wiley series in probability and statistics Probability and statistics section
Soggetto topico Multivariate analysis
Regression analysis - Graphic methods
ISBN 1-282-30757-6
9786612307577
0-470-31693-4
0-470-31777-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Regression Graphics Ideas for Studying Regressions through Graphics; Contents; Preface; 1. Introduction; 1.1. C.C & I,1; 1.1.1. Construction; 1.1.3. Inference; 1.1.2. Characterization; 1.2. Illustrations; 1.2.1. Residuals versus fitted values; 1.2.2. Residuals versus the predictors; 1.2.3. Residuals versus the response; 1.3. On things to come; 1.4. Notational conventions; Problems; 2. Introduction to 2D Scatterplots; 2.1. Response plots in simple regression; 2.2. New Zealand horse mussels; 2.3. Transforming y via inverse response plots; 2.3.1 Response transformations
2.3.2 Response transformations: Mussel data2.4. Danish twins; 2.5. Scatterplot matrices; 2.5.1 Consrruction; 2.5.2 Example; 2.6. Regression graphics in the 1920s; 2.6.1. Ezekiel's successive approximations; 2.6.2. Bean's graphic method; 2.7. Discussion; Problems; 3. Constructing 3D Scatterplots; 3.1. Getting an impression of 3D; 3.2. Depth cuing; 3.3. Scaling; 3.4. Orthogonalization; Problems; 4. Interpreting 3D Scatterplots; 4.1. Haystacks; 4.2. Structural dimensionality; 4.2.1. One predictor; 4.2.2. Two predictors; 4.2.3 Many predictors; 4.3. One-dimensional structure
4.4. Two-dimensional structure4.4.1. Removing linear trends; 4.4.2. Identifying semiparametric regression functions; 4.5. Assessing structural dimensionality; 4.5.1. A visual metaphor for structural dimension; 4.5.2. A first method for deciding d = 1 or 2; 4.5.3. Natural rubber; 4.6. Assessment methods; 4.6.1. Using independence; 4.6.2. Using uncorrelated 2D views; 4.6.3. Uncorrelated 2D views: Haystack data; 4.6.4. Intraslice residuals; 4.6.5. Intraslice orthogonalization; 4.6.6. Mussels again; 4.6.7. Discussion; Problems; 5. Binary Response Variables; 5.1. One predictor; 5.2. Two predictors
7.5.2 Conditions for S ylx1=S(n1)
Record Nr. UNINA-9910841509503321
Cook R. Dennis  
New York, : Wiley, c1998
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Robust regression and outlier detection / Peter J. Rousseeuw, Annick M. Leroy
Robust regression and outlier detection / Peter J. Rousseeuw, Annick M. Leroy
Autore Rousseeuw, Peter J
Pubbl/distr/stampa New York [etc.] : Wiley, c1987
Descrizione fisica xiv, 329 p. ; 23 cm
Disciplina 519.536
Altri autori (Persone) Leroy, Annick M
Collana Wiley series in probability and mathematical statistics
Soggetto topico Analisi della regressione
Statistica matematica
ISBN 0471852333
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991000716459707536
Rousseeuw, Peter J  
New York [etc.] : Wiley, c1987
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Autore Keele Luke <1974->
Pubbl/distr/stampa Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Descrizione fisica 1 online resource (231 p.)
Disciplina 300.1519536
519.5/36
519.536
Soggetto topico Regression analysis
Nonparametric statistics
Soggetto genere / forma Electronic books.
ISBN 1-281-31237-1
9786611312374
0-470-99813-X
0-470-99812-1
Classificazione 31.73
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Semiparametric Regression for the Social Sciences; Contents; List of Tables; List of Figures; Preface; 1 Introduction: Global versus Local Statistics; 1.1 The Consequences of Ignoring Nonlinearity; 1.2 Power Transformations; 1.3 Nonparametric and Semiparametric Techniques; 1.4 Outline of the Text; 2 Smoothing and Local Regression; 2.1 Simple Smoothing; 2.1.1 Local Averaging; 2.1.2 Kernel Smoothing; 2.2 Local Polynomial Regression; 2.3 Nonparametric Modeling Choices; 2.3.1 The Span; 2.3.2 Polynomial Degree andWeight Function; 2.3.3 A Note on Interpretation
2.4 Statistical Inference for Local Polynomial Regression2.5 Multiple Nonparametric Regression; 2.6 Conclusion; 2.7 Exercises; 3 Splines; 3.1 Simple Regression Splines; 3.1.1 Basis Functions; 3.2 Other Spline Models and Bases; 3.2.1 Quadratic and Cubic Spline Bases; 3.2.2 Natural Splines; 3.2.3 B-splines; 3.2.4 Knot Placement and Numbers; 3.2.5 Comparing Spline Models; 3.3 Splines and Over.tting; 3.3.1 Smoothing Splines; 3.3.2 Splines as Mixed Models; 3.3.3 Final Notes on Smoothing Splines; 3.3.4 Thin Plate Splines; 3.4 Inference for Splines; 3.5 Comparisons and Conclusions; 3.6 Exercises
4 Automated Smoothing Techniques4.1 Span by Cross-Validation; 4.2 Splines and Automated Smoothing; 4.2.1 Estimating Smoothing Through the Likelihood; 4.2.2 Smoothing Splines and Cross-Validation; 4.3 Automated Smoothing in Practice; 4.4 Automated Smoothing Caveats; 4.5 Exercises; 5 Additive and Semiparametric Regression Models; 5.1 Additive Models; 5.2 Semiparametric Regression Models; 5.3 Estimation; 5.3.1 Back.tting; 5.4 Inference; 5.5 Examples; 5.5.1 Congressional Elections; 5.5.2 Feminist Attitudes; 5.6 Discussion; 5.7 Exercises; 6 Generalized Additive Models
6.1 Generalized Linear Models6.2 Estimation of GAMS; 6.3 Statistical Inference; 6.4 Examples; 6.4.1 Logistic Regression: The Liberal Peace; 6.4.2 Ordered Logit: Domestic Violence; 6.4.3 Count Models: Supreme Court Overrides; 6.4.4 Survival Models: Race Riots; 6.5 Discussion; 6.6 Exercises; 7 Extensions of the Semiparametric Regression Model; 7.1 Mixed Models; 7.2 Bayesian Smoothing; 7.3 Propensity Score Matching; 7.4 Conclusion; 8 Bootstrapping; 8.1 Classical Inference; 8.2 Bootstrapping - An Overview; 8.2.1 Bootstrapping; 8.2.2 An Example: Bootstrapping the Mean
8.2.3 Bootstrapping Regression Models8.2.4 An Example: Presidential Elections; 8.3 Bootstrapping Nonparametric and Semiparametric Regression Models; 8.3.1 Bootstrapping Nonparametric Fits; 8.3.2 Bootstrapping Nonlinearity Tests; 8.4 Conclusion; 8.5 Exercises; 9 Epilogue; Appendix: Software; Bibliography; Author's Index; Subject Index
Record Nr. UNINA-9910144715103321
Keele Luke <1974->  
Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Autore Keele Luke <1974->
Pubbl/distr/stampa Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Descrizione fisica 1 online resource (231 p.)
Disciplina 300.1519536
519.5/36
519.536
Soggetto topico Regression analysis
Nonparametric statistics
ISBN 1-281-31237-1
9786611312374
0-470-99813-X
0-470-99812-1
Classificazione 31.73
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Semiparametric Regression for the Social Sciences; Contents; List of Tables; List of Figures; Preface; 1 Introduction: Global versus Local Statistics; 1.1 The Consequences of Ignoring Nonlinearity; 1.2 Power Transformations; 1.3 Nonparametric and Semiparametric Techniques; 1.4 Outline of the Text; 2 Smoothing and Local Regression; 2.1 Simple Smoothing; 2.1.1 Local Averaging; 2.1.2 Kernel Smoothing; 2.2 Local Polynomial Regression; 2.3 Nonparametric Modeling Choices; 2.3.1 The Span; 2.3.2 Polynomial Degree andWeight Function; 2.3.3 A Note on Interpretation
2.4 Statistical Inference for Local Polynomial Regression2.5 Multiple Nonparametric Regression; 2.6 Conclusion; 2.7 Exercises; 3 Splines; 3.1 Simple Regression Splines; 3.1.1 Basis Functions; 3.2 Other Spline Models and Bases; 3.2.1 Quadratic and Cubic Spline Bases; 3.2.2 Natural Splines; 3.2.3 B-splines; 3.2.4 Knot Placement and Numbers; 3.2.5 Comparing Spline Models; 3.3 Splines and Over.tting; 3.3.1 Smoothing Splines; 3.3.2 Splines as Mixed Models; 3.3.3 Final Notes on Smoothing Splines; 3.3.4 Thin Plate Splines; 3.4 Inference for Splines; 3.5 Comparisons and Conclusions; 3.6 Exercises
4 Automated Smoothing Techniques4.1 Span by Cross-Validation; 4.2 Splines and Automated Smoothing; 4.2.1 Estimating Smoothing Through the Likelihood; 4.2.2 Smoothing Splines and Cross-Validation; 4.3 Automated Smoothing in Practice; 4.4 Automated Smoothing Caveats; 4.5 Exercises; 5 Additive and Semiparametric Regression Models; 5.1 Additive Models; 5.2 Semiparametric Regression Models; 5.3 Estimation; 5.3.1 Back.tting; 5.4 Inference; 5.5 Examples; 5.5.1 Congressional Elections; 5.5.2 Feminist Attitudes; 5.6 Discussion; 5.7 Exercises; 6 Generalized Additive Models
6.1 Generalized Linear Models6.2 Estimation of GAMS; 6.3 Statistical Inference; 6.4 Examples; 6.4.1 Logistic Regression: The Liberal Peace; 6.4.2 Ordered Logit: Domestic Violence; 6.4.3 Count Models: Supreme Court Overrides; 6.4.4 Survival Models: Race Riots; 6.5 Discussion; 6.6 Exercises; 7 Extensions of the Semiparametric Regression Model; 7.1 Mixed Models; 7.2 Bayesian Smoothing; 7.3 Propensity Score Matching; 7.4 Conclusion; 8 Bootstrapping; 8.1 Classical Inference; 8.2 Bootstrapping - An Overview; 8.2.1 Bootstrapping; 8.2.2 An Example: Bootstrapping the Mean
8.2.3 Bootstrapping Regression Models8.2.4 An Example: Presidential Elections; 8.3 Bootstrapping Nonparametric and Semiparametric Regression Models; 8.3.1 Bootstrapping Nonparametric Fits; 8.3.2 Bootstrapping Nonlinearity Tests; 8.4 Conclusion; 8.5 Exercises; 9 Epilogue; Appendix: Software; Bibliography; Author's Index; Subject Index
Record Nr. UNINA-9910829940503321
Keele Luke <1974->  
Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Semiparametric regression for the social sciences [[electronic resource] /] / Luke Keele
Autore Keele Luke <1974->
Pubbl/distr/stampa Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Descrizione fisica 1 online resource (231 p.)
Disciplina 300.1519536
519.5/36
519.536
Soggetto topico Regression analysis
Nonparametric statistics
ISBN 1-281-31237-1
9786611312374
0-470-99813-X
0-470-99812-1
Classificazione 31.73
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Semiparametric Regression for the Social Sciences; Contents; List of Tables; List of Figures; Preface; 1 Introduction: Global versus Local Statistics; 1.1 The Consequences of Ignoring Nonlinearity; 1.2 Power Transformations; 1.3 Nonparametric and Semiparametric Techniques; 1.4 Outline of the Text; 2 Smoothing and Local Regression; 2.1 Simple Smoothing; 2.1.1 Local Averaging; 2.1.2 Kernel Smoothing; 2.2 Local Polynomial Regression; 2.3 Nonparametric Modeling Choices; 2.3.1 The Span; 2.3.2 Polynomial Degree andWeight Function; 2.3.3 A Note on Interpretation
2.4 Statistical Inference for Local Polynomial Regression2.5 Multiple Nonparametric Regression; 2.6 Conclusion; 2.7 Exercises; 3 Splines; 3.1 Simple Regression Splines; 3.1.1 Basis Functions; 3.2 Other Spline Models and Bases; 3.2.1 Quadratic and Cubic Spline Bases; 3.2.2 Natural Splines; 3.2.3 B-splines; 3.2.4 Knot Placement and Numbers; 3.2.5 Comparing Spline Models; 3.3 Splines and Over.tting; 3.3.1 Smoothing Splines; 3.3.2 Splines as Mixed Models; 3.3.3 Final Notes on Smoothing Splines; 3.3.4 Thin Plate Splines; 3.4 Inference for Splines; 3.5 Comparisons and Conclusions; 3.6 Exercises
4 Automated Smoothing Techniques4.1 Span by Cross-Validation; 4.2 Splines and Automated Smoothing; 4.2.1 Estimating Smoothing Through the Likelihood; 4.2.2 Smoothing Splines and Cross-Validation; 4.3 Automated Smoothing in Practice; 4.4 Automated Smoothing Caveats; 4.5 Exercises; 5 Additive and Semiparametric Regression Models; 5.1 Additive Models; 5.2 Semiparametric Regression Models; 5.3 Estimation; 5.3.1 Back.tting; 5.4 Inference; 5.5 Examples; 5.5.1 Congressional Elections; 5.5.2 Feminist Attitudes; 5.6 Discussion; 5.7 Exercises; 6 Generalized Additive Models
6.1 Generalized Linear Models6.2 Estimation of GAMS; 6.3 Statistical Inference; 6.4 Examples; 6.4.1 Logistic Regression: The Liberal Peace; 6.4.2 Ordered Logit: Domestic Violence; 6.4.3 Count Models: Supreme Court Overrides; 6.4.4 Survival Models: Race Riots; 6.5 Discussion; 6.6 Exercises; 7 Extensions of the Semiparametric Regression Model; 7.1 Mixed Models; 7.2 Bayesian Smoothing; 7.3 Propensity Score Matching; 7.4 Conclusion; 8 Bootstrapping; 8.1 Classical Inference; 8.2 Bootstrapping - An Overview; 8.2.1 Bootstrapping; 8.2.2 An Example: Bootstrapping the Mean
8.2.3 Bootstrapping Regression Models8.2.4 An Example: Presidential Elections; 8.3 Bootstrapping Nonparametric and Semiparametric Regression Models; 8.3.1 Bootstrapping Nonparametric Fits; 8.3.2 Bootstrapping Nonlinearity Tests; 8.4 Conclusion; 8.5 Exercises; 9 Epilogue; Appendix: Software; Bibliography; Author's Index; Subject Index
Record Nr. UNINA-9910841711803321
Keele Luke <1974->  
Chichester, England ; ; Hoboken, NJ, : Wiley, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Sensitivity analysis in linear regression [[electronic resource] /] / Samprit Chatterjee, Ali S. Hadi
Sensitivity analysis in linear regression [[electronic resource] /] / Samprit Chatterjee, Ali S. Hadi
Autore Chatterjee Samprit <1938->
Pubbl/distr/stampa New York, : Wiley, c1988
Descrizione fisica 1 online resource (341 p.)
Disciplina 519.5
519.536
Altri autori (Persone) HadiAli S
Collana Wiley series in probability and mathematical statistics. Applied probability and statistics
Soggetto topico Regression analysis
Perturbation (Mathematics)
Mathematical optimization
ISBN 1-282-30736-3
9786612307362
0-470-31676-4
0-470-31742-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Sensitivity Analysis in Linear Regression; PREFACE; Contents; 1. INTRODUCTION; 1.1. Introduction; 1.2. Notations; 1.3. Standard Estimation Results in Least Squares; 1.4. Assumptions; 1.5. Iterative Regression Process; 1.6. Organization of the Book; 2. PREDICTION MATRIX; 2.1.Introduction; 2.2. Roles of P and (I -P) in Linear Regression; 2.3. Properties of the Prediction Matrix; 2.3.1. General Properties; 2.3.2. Omitting (Adding) Variables; 2.3.3. Omitting (Adding) an Observation; 2.3.4. Conditions for Large Values of pii; 2.3.5. Omitting Multiple Rows of X; 2.3.6. Eigenvalues of P and (I- P)
2.3.7. Distribution of pü2.4. Examples; 3. ROLE OF VARIABLES IN A REGRESSION EQUATION; 3.1. Introduction; 3.2. Effects of Underfitting; 3.3. Effects of Overfining; 3.4. Interpreting Successive Fining; 3.5. Computing Implications for Successive Fitting; 3.6. Introduction of One Additional Regressor; 3.7. Comparing Models: Comparison Criteria; 3.8. Diagnostic Plots for the Effects of Variables; 3.8.1. Added Variable (Partial Regression) Plots; 3.8.2. Residual Versus Predictor Plots; 3.8.3. Component-Plus-Residual (Partial Residual) Plots; 3.8.4. Augmented Partial Residual Plots
3.9. Effects of an Additional Regressor4. EFFECTS OF AN OBSERVATION ON A REGRESSION EQUATION; 4.1. Introduction; 4.2. Omission Approach; 4.2.1. Measures Based on Residuals; 4.2.1.1. Testing for a Single Outlier; 4.2.1.2. Graphical Methods; 4.2.2. Outliers, High-leverage, and Influential Points; 4.2.3. Measures Based on Remoteness of Points in X-Y Space; 4.2.3.1. Diagonal Elements of P; 4.2.3.2. Mahalanobis Distance; 4.2.3.3. Weighted Squared Standardized Distance; 4.2.3.4. Diagonal Elements of Pz; 4.2.4. Influence Curve; 4.2.4.1. Definition of the Influence Curve
4.2.4.2. Influence Curves for β and σ24.2.4.3. Approximating the Influence Curve; 4.2.5. Measures Based on the Influence Curve; 4.2.5.1. Cook's Distance; 4.2.5.2. Welsch-Kuh's Distance; 4.2.5.3. Welsch's Distance; 4.2.5.4. Modified Cooks Distance; 4.2.6. Measures Based on the Volume of Confidence Ellipsoids; 4.2.6.1. Andrews-Pregibon Statistic; 4.2.6.2. Variance Ratio; 4.2.6.3. Cook-Weisberg Statistic; 4.2.7. Measures Based on the Likelihood Function; 4.2.8. Measures Based on a Subset of the Regression Coefficients; 4.2.8.1. Influence on a Single Regression Coefficient
4.2.8.2. Ilnfluence on Linear Functions of β4.2.9. Measures based on the Eigensmcture of X; 4.2.9.1. Condition Number and Collinearity Indices; 4.2.9.2. Collinearity-Influential Points; 4.2.9.3. Effects of an Observation on the Condition Number; 4.2.9.4. Diagnosing Collinearhy-Influential Observations; 4.3. Differentiation Approach; 4.4. Summary and Concluding Remarks; 5. ASSESSING THE EFFECTS OF MULTIPLE OBSERVATIONS; 5.1. Introduction; 5.2. Measures Based on Residuals; 5.3. Measures Based on the Influence Curve; 5.3.1. Sample lnfluence Curve; 5.3.2. Empirical Influence Curve
5.3.3. Generalized Cook's Distance
Record Nr. UNINA-9910144694903321
Chatterjee Samprit <1938->  
New York, : Wiley, c1988
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Sensitivity analysis in linear regression [[electronic resource] /] / Samprit Chatterjee, Ali S. Hadi
Sensitivity analysis in linear regression [[electronic resource] /] / Samprit Chatterjee, Ali S. Hadi
Autore Chatterjee Samprit <1938->
Pubbl/distr/stampa New York, : Wiley, c1988
Descrizione fisica 1 online resource (341 p.)
Disciplina 519.5
519.536
Altri autori (Persone) HadiAli S
Collana Wiley series in probability and mathematical statistics. Applied probability and statistics
Soggetto topico Regression analysis
Perturbation (Mathematics)
Mathematical optimization
ISBN 1-282-30736-3
9786612307362
0-470-31676-4
0-470-31742-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Sensitivity Analysis in Linear Regression; PREFACE; Contents; 1. INTRODUCTION; 1.1. Introduction; 1.2. Notations; 1.3. Standard Estimation Results in Least Squares; 1.4. Assumptions; 1.5. Iterative Regression Process; 1.6. Organization of the Book; 2. PREDICTION MATRIX; 2.1.Introduction; 2.2. Roles of P and (I -P) in Linear Regression; 2.3. Properties of the Prediction Matrix; 2.3.1. General Properties; 2.3.2. Omitting (Adding) Variables; 2.3.3. Omitting (Adding) an Observation; 2.3.4. Conditions for Large Values of pii; 2.3.5. Omitting Multiple Rows of X; 2.3.6. Eigenvalues of P and (I- P)
2.3.7. Distribution of pü2.4. Examples; 3. ROLE OF VARIABLES IN A REGRESSION EQUATION; 3.1. Introduction; 3.2. Effects of Underfitting; 3.3. Effects of Overfining; 3.4. Interpreting Successive Fining; 3.5. Computing Implications for Successive Fitting; 3.6. Introduction of One Additional Regressor; 3.7. Comparing Models: Comparison Criteria; 3.8. Diagnostic Plots for the Effects of Variables; 3.8.1. Added Variable (Partial Regression) Plots; 3.8.2. Residual Versus Predictor Plots; 3.8.3. Component-Plus-Residual (Partial Residual) Plots; 3.8.4. Augmented Partial Residual Plots
3.9. Effects of an Additional Regressor4. EFFECTS OF AN OBSERVATION ON A REGRESSION EQUATION; 4.1. Introduction; 4.2. Omission Approach; 4.2.1. Measures Based on Residuals; 4.2.1.1. Testing for a Single Outlier; 4.2.1.2. Graphical Methods; 4.2.2. Outliers, High-leverage, and Influential Points; 4.2.3. Measures Based on Remoteness of Points in X-Y Space; 4.2.3.1. Diagonal Elements of P; 4.2.3.2. Mahalanobis Distance; 4.2.3.3. Weighted Squared Standardized Distance; 4.2.3.4. Diagonal Elements of Pz; 4.2.4. Influence Curve; 4.2.4.1. Definition of the Influence Curve
4.2.4.2. Influence Curves for β and σ24.2.4.3. Approximating the Influence Curve; 4.2.5. Measures Based on the Influence Curve; 4.2.5.1. Cook's Distance; 4.2.5.2. Welsch-Kuh's Distance; 4.2.5.3. Welsch's Distance; 4.2.5.4. Modified Cooks Distance; 4.2.6. Measures Based on the Volume of Confidence Ellipsoids; 4.2.6.1. Andrews-Pregibon Statistic; 4.2.6.2. Variance Ratio; 4.2.6.3. Cook-Weisberg Statistic; 4.2.7. Measures Based on the Likelihood Function; 4.2.8. Measures Based on a Subset of the Regression Coefficients; 4.2.8.1. Influence on a Single Regression Coefficient
4.2.8.2. Ilnfluence on Linear Functions of β4.2.9. Measures based on the Eigensmcture of X; 4.2.9.1. Condition Number and Collinearity Indices; 4.2.9.2. Collinearity-Influential Points; 4.2.9.3. Effects of an Observation on the Condition Number; 4.2.9.4. Diagnosing Collinearhy-Influential Observations; 4.3. Differentiation Approach; 4.4. Summary and Concluding Remarks; 5. ASSESSING THE EFFECTS OF MULTIPLE OBSERVATIONS; 5.1. Introduction; 5.2. Measures Based on Residuals; 5.3. Measures Based on the Influence Curve; 5.3.1. Sample lnfluence Curve; 5.3.2. Empirical Influence Curve
5.3.3. Generalized Cook's Distance
Record Nr. UNINA-9910830090503321
Chatterjee Samprit <1938->  
New York, : Wiley, c1988
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Sensitivity analysis in linear regression [[electronic resource] /] / Samprit Chatterjee, Ali S. Hadi
Sensitivity analysis in linear regression [[electronic resource] /] / Samprit Chatterjee, Ali S. Hadi
Autore Chatterjee Samprit <1938->
Pubbl/distr/stampa New York, : Wiley, c1988
Descrizione fisica 1 online resource (341 p.)
Disciplina 519.5
519.536
Altri autori (Persone) HadiAli S
Collana Wiley series in probability and mathematical statistics. Applied probability and statistics
Soggetto topico Regression analysis
Perturbation (Mathematics)
Mathematical optimization
ISBN 1-282-30736-3
9786612307362
0-470-31676-4
0-470-31742-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Sensitivity Analysis in Linear Regression; PREFACE; Contents; 1. INTRODUCTION; 1.1. Introduction; 1.2. Notations; 1.3. Standard Estimation Results in Least Squares; 1.4. Assumptions; 1.5. Iterative Regression Process; 1.6. Organization of the Book; 2. PREDICTION MATRIX; 2.1.Introduction; 2.2. Roles of P and (I -P) in Linear Regression; 2.3. Properties of the Prediction Matrix; 2.3.1. General Properties; 2.3.2. Omitting (Adding) Variables; 2.3.3. Omitting (Adding) an Observation; 2.3.4. Conditions for Large Values of pii; 2.3.5. Omitting Multiple Rows of X; 2.3.6. Eigenvalues of P and (I- P)
2.3.7. Distribution of pü2.4. Examples; 3. ROLE OF VARIABLES IN A REGRESSION EQUATION; 3.1. Introduction; 3.2. Effects of Underfitting; 3.3. Effects of Overfining; 3.4. Interpreting Successive Fining; 3.5. Computing Implications for Successive Fitting; 3.6. Introduction of One Additional Regressor; 3.7. Comparing Models: Comparison Criteria; 3.8. Diagnostic Plots for the Effects of Variables; 3.8.1. Added Variable (Partial Regression) Plots; 3.8.2. Residual Versus Predictor Plots; 3.8.3. Component-Plus-Residual (Partial Residual) Plots; 3.8.4. Augmented Partial Residual Plots
3.9. Effects of an Additional Regressor4. EFFECTS OF AN OBSERVATION ON A REGRESSION EQUATION; 4.1. Introduction; 4.2. Omission Approach; 4.2.1. Measures Based on Residuals; 4.2.1.1. Testing for a Single Outlier; 4.2.1.2. Graphical Methods; 4.2.2. Outliers, High-leverage, and Influential Points; 4.2.3. Measures Based on Remoteness of Points in X-Y Space; 4.2.3.1. Diagonal Elements of P; 4.2.3.2. Mahalanobis Distance; 4.2.3.3. Weighted Squared Standardized Distance; 4.2.3.4. Diagonal Elements of Pz; 4.2.4. Influence Curve; 4.2.4.1. Definition of the Influence Curve
4.2.4.2. Influence Curves for β and σ24.2.4.3. Approximating the Influence Curve; 4.2.5. Measures Based on the Influence Curve; 4.2.5.1. Cook's Distance; 4.2.5.2. Welsch-Kuh's Distance; 4.2.5.3. Welsch's Distance; 4.2.5.4. Modified Cooks Distance; 4.2.6. Measures Based on the Volume of Confidence Ellipsoids; 4.2.6.1. Andrews-Pregibon Statistic; 4.2.6.2. Variance Ratio; 4.2.6.3. Cook-Weisberg Statistic; 4.2.7. Measures Based on the Likelihood Function; 4.2.8. Measures Based on a Subset of the Regression Coefficients; 4.2.8.1. Influence on a Single Regression Coefficient
4.2.8.2. Ilnfluence on Linear Functions of β4.2.9. Measures based on the Eigensmcture of X; 4.2.9.1. Condition Number and Collinearity Indices; 4.2.9.2. Collinearity-Influential Points; 4.2.9.3. Effects of an Observation on the Condition Number; 4.2.9.4. Diagnosing Collinearhy-Influential Observations; 4.3. Differentiation Approach; 4.4. Summary and Concluding Remarks; 5. ASSESSING THE EFFECTS OF MULTIPLE OBSERVATIONS; 5.1. Introduction; 5.2. Measures Based on Residuals; 5.3. Measures Based on the Influence Curve; 5.3.1. Sample lnfluence Curve; 5.3.2. Empirical Influence Curve
5.3.3. Generalized Cook's Distance
Record Nr. UNINA-9910841885803321
Chatterjee Samprit <1938->  
New York, : Wiley, c1988
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui