LEADER 04927nam 2200661 a 450 001 9910139720503321 005 20180612235026.0 010 $a1-283-44611-1 010 $a9786613446114 010 $a1-118-15065-1 010 $a1-118-15064-3 035 $a(CKB)2550000000079880 035 $a(EBL)836550 035 $a(OCoLC)774272123 035 $a(SSID)ssj0000593362 035 $a(PQKBManifestationID)11367350 035 $a(PQKBTitleCode)TC0000593362 035 $a(PQKBWorkID)10736776 035 $a(PQKB)10498333 035 $a(MiAaPQ)EBC836550 035 $a(EXLCZ)992550000000079880 100 $a19990311d2000 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSmoothing and regression$b[electronic resource] $eapproaches, computation, and application /$fedited by Michael G. Schimek 210 $aNew York $cWiley$d2000 215 $a1 online resource (648 p.) 225 1 $aWiley series in probability and mathematical statistics. Applied probability and statistics section 300 $a"A Wiley-Interscience Publication." 311 $a0-471-17946-9 320 $aIncludes bibliographical references and index. 327 $aSmoothing 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 ?2; 2.5 Determination of ?; 2.6 Estimation of ?2 327 $a4.2 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 327 $a7. 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 327 $a9.3 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 327 $a11.5 Computation 330 $aA comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regressionSmoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly technical and specialized, this book discusses all methods in light of both computational efficiency and their applicab 410 0$aWiley series in probability and statistics.$pApplied probability and statistics. 606 $aSmoothing (Statistics) 606 $aNonparametric statistics 606 $aRegression analysis 608 $aElectronic books. 615 0$aSmoothing (Statistics) 615 0$aNonparametric statistics. 615 0$aRegression analysis. 676 $a519.5/36 676 $a519.536 701 $aSchimek$b Michael G$0140075 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139720503321 996 $aSmoothing and regression$92111211 997 $aUNINA