04927nam 2200661 a 450 991013972050332120180612235026.01-283-44611-197866134461141-118-15065-11-118-15064-3(CKB)2550000000079880(EBL)836550(OCoLC)774272123(SSID)ssj0000593362(PQKBManifestationID)11367350(PQKBTitleCode)TC0000593362(PQKBWorkID)10736776(PQKB)10498333(MiAaPQ)EBC836550(EXLCZ)99255000000007988019990311d2000 uy 0engur|n|---|||||txtccrSmoothing and regression[electronic resource] approaches, computation, and application /edited by Michael G. SchimekNew York Wiley20001 online resource (648 p.)Wiley series in probability and mathematical statistics. Applied probability and statistics section"A Wiley-Interscience Publication."0-471-17946-9 Includes bibliographical references and index.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 τ24.2 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; References7. 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 Fitting9.3 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 Splines11.5 ComputationA comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regressionSmoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly technical and specialized, this book discusses all methods in light of both computational efficiency and their applicabWiley series in probability and statistics.Applied probability and statistics.Smoothing (Statistics)Nonparametric statisticsRegression analysisElectronic books.Smoothing (Statistics)Nonparametric statistics.Regression analysis.519.5/36519.536Schimek Michael G140075MiAaPQMiAaPQMiAaPQBOOK9910139720503321Smoothing and regression2111211UNINA