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Smoothing and regression [[electronic resource] ] : approaches, computation, and application / / edited by Michael G. Schimek
| Smoothing and regression [[electronic resource] ] : approaches, computation, and application / / edited by Michael G. Schimek |
| Pubbl/distr/stampa | New York, : Wiley, 2000 |
| Descrizione fisica | 1 online resource (648 p.) |
| Disciplina |
519.5/36
519.536 |
| Altri autori (Persone) | SchimekMichael G |
| Collana | Wiley series in probability and mathematical statistics. Applied probability and statistics section |
| Soggetto topico |
Smoothing (Statistics)
Nonparametric statistics Regression analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-44611-1
9786613446114 1-118-15065-1 1-118-15064-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Smoothing and Regression: Approaches, Computation, and Application; Contents; Foreword; Preface; 1. Spline Regression; 1.1 Introduction; 1.2 General Form of the Estimator; 1.3 The Linear Smoothing Spline; 1.4 Large-Sample Efficiency; 1.5 Bayesian Motivation; 1.6 Extensions and Implementations; References; 2. Variance Estimation and Smoothing-Parameter Selection for Spline Regression; 2.1 Introduction and Some Definitions; 2.2 Interpretation of the Smoothing Parameter; 2.3 Quantifying the Complexity of a Smoothing Spline; 2.4 Estimation of σ2; 2.5 Determination of λ; 2.6 Estimation of τ2
4.2 Nonparametric Variance Estimators4.3 Bandwidth Choice for Kernel Regression Estimators; References; 5. Spline and Kernel Regression under Shape Restrictions; 5.1 Introduction; 5.2 Description of the Main Methods; 5.3 A Comparative View; 5.4 Examples; 5.5 Software Hints; References; 6. Spline and Kernel Regression for Dependent Data; 6.1 Introduction; 6.2 Approaches for a Known Autocorrelation Function; 6.3 Approaches for an Unknown Autocorrelation Function; 6.4 A Bayesian Approach to Smoothing Dependent Data; 6.5 Applications of Smoothing Dependent Data; References 7. Wavelets for Regression and Other Statistical Problems7.1 Introduction; 7.2 Wavelet Expansions; 7.3 The Discrete Wavelet Transform in S; 7.4 Wavelet Shrinkage; 7.5 Estimators for Data With Correlated Noise; 7.6 Implementation of the Wavelet Transform; 7.7 How to Obtain and Install the Wavelet Software; References; 8. Smoothing Methods for Discrete Data; 8.1 Introduction; 8.2 Smoothing Contingency Tables; 8.3 Smoothing Approaches to Categorical Regression; 8.4 Conclusion; References; 9. Local Polynomial Fitting; 9.1 Introduction; 9.2 Properties of Local Polynomial Fitting 9.3 Choice of Bandwidth9.4 Choice of the Degree; 9.5 Local Modeling; 9.6 Some More Applications; References; 10. Additive and Generalized Additive Models; 10.1 Introduction; 10.2 The Additive Model; 10.3 Generalized Additive Models; 10.4 Alternating Conditional Expectations Additivity, and Variance Stabilization; 10.5 Smoothing Parameter and Bandwidth Determination; 10.6 Model Diagnostics; 10.7 New Developments; References; 11. Multivariate Spline Regression; 11.1 Introduction; 11.2 Smoothing Splines as Bayes Estimates; 11.3 ANOVA Decomposition on Product Domains; 11.4 Tensor Product Splines 11.5 Computation |
| Record Nr. | UNINA-9910139720503321 |
| New York, : Wiley, 2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Smoothing and regression [[electronic resource] ] : approaches, computation, and application / / edited by Michael G. Schimek
| Smoothing and regression [[electronic resource] ] : approaches, computation, and application / / edited by Michael G. Schimek |
| Pubbl/distr/stampa | New York, : Wiley, 2000 |
| Descrizione fisica | 1 online resource (648 p.) |
| Disciplina |
519.5/36
519.536 |
| Altri autori (Persone) | SchimekMichael G |
| Collana | Wiley series in probability and mathematical statistics. Applied probability and statistics section |
| Soggetto topico |
Smoothing (Statistics)
Nonparametric statistics Regression analysis |
| ISBN |
1-283-44611-1
9786613446114 1-118-15065-1 1-118-15064-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Smoothing and Regression: Approaches, Computation, and Application; Contents; Foreword; Preface; 1. Spline Regression; 1.1 Introduction; 1.2 General Form of the Estimator; 1.3 The Linear Smoothing Spline; 1.4 Large-Sample Efficiency; 1.5 Bayesian Motivation; 1.6 Extensions and Implementations; References; 2. Variance Estimation and Smoothing-Parameter Selection for Spline Regression; 2.1 Introduction and Some Definitions; 2.2 Interpretation of the Smoothing Parameter; 2.3 Quantifying the Complexity of a Smoothing Spline; 2.4 Estimation of σ2; 2.5 Determination of λ; 2.6 Estimation of τ2
4.2 Nonparametric Variance Estimators4.3 Bandwidth Choice for Kernel Regression Estimators; References; 5. Spline and Kernel Regression under Shape Restrictions; 5.1 Introduction; 5.2 Description of the Main Methods; 5.3 A Comparative View; 5.4 Examples; 5.5 Software Hints; References; 6. Spline and Kernel Regression for Dependent Data; 6.1 Introduction; 6.2 Approaches for a Known Autocorrelation Function; 6.3 Approaches for an Unknown Autocorrelation Function; 6.4 A Bayesian Approach to Smoothing Dependent Data; 6.5 Applications of Smoothing Dependent Data; References 7. Wavelets for Regression and Other Statistical Problems7.1 Introduction; 7.2 Wavelet Expansions; 7.3 The Discrete Wavelet Transform in S; 7.4 Wavelet Shrinkage; 7.5 Estimators for Data With Correlated Noise; 7.6 Implementation of the Wavelet Transform; 7.7 How to Obtain and Install the Wavelet Software; References; 8. Smoothing Methods for Discrete Data; 8.1 Introduction; 8.2 Smoothing Contingency Tables; 8.3 Smoothing Approaches to Categorical Regression; 8.4 Conclusion; References; 9. Local Polynomial Fitting; 9.1 Introduction; 9.2 Properties of Local Polynomial Fitting 9.3 Choice of Bandwidth9.4 Choice of the Degree; 9.5 Local Modeling; 9.6 Some More Applications; References; 10. Additive and Generalized Additive Models; 10.1 Introduction; 10.2 The Additive Model; 10.3 Generalized Additive Models; 10.4 Alternating Conditional Expectations Additivity, and Variance Stabilization; 10.5 Smoothing Parameter and Bandwidth Determination; 10.6 Model Diagnostics; 10.7 New Developments; References; 11. Multivariate Spline Regression; 11.1 Introduction; 11.2 Smoothing Splines as Bayes Estimates; 11.3 ANOVA Decomposition on Product Domains; 11.4 Tensor Product Splines 11.5 Computation |
| Record Nr. | UNINA-9910830990403321 |
| New York, : Wiley, 2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||