LEADER 04893nam 2200649 a 450 001 9910830990403321 005 20230422050657.0 010 $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 $aRegression analysis 615 0$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 $a9910830990403321 996 $aSmoothing and regression$94111452 997 $aUNINA LEADER 01898oam 2200493zu 450 001 9910376233703321 005 20210807002008.0 035 $a(CKB)3170000000001967 035 $a(SSID)ssj0001138486 035 $a(PQKBManifestationID)11775305 035 $a(PQKBTitleCode)TC0001138486 035 $a(PQKBWorkID)11170990 035 $a(PQKB)11020697 035 $a(WaSeSS)IndRDA00014392 035 $a(Association for Computing Machinery)10.1145/379539 035 $a(EXLCZ)993170000000001967 100 $a20160829d2001 uy 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProceedings of the eighth ACM SIGPLAN Symposium on Principles and Practices of Parallel Programming 210 31$a[Place of publication not identified]$cACM$d2001 215 $a1 online resource (142 pages) 225 1 $aACM Conferences 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-58113-346-4 410 0$aACM Conferences 517 1 $aProceedings of the eighth Association for Computing Machinery Special Interest Group on Programming Languages Symposium on Principles and Practices of Parallel Programming 517 1 $aPPoPP '01 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Snowbird, UT, USA - June 18 - 20, 2001 606 $aEngineering & Applied Sciences$2HILCC 606 $aComputer Science$2HILCC 615 7$aEngineering & Applied Sciences 615 7$aComputer Science 700 $aHeath$b Michael$0173043 702 $aLumsdaine$b Andrew 702 $aHeath$b Michael T 712 02$aAssociation for Computing Machinery-Digital Library. 801 0$bPQKB 906 $aBOOK 912 $a9910376233703321 996 $aProceedings of the eighth ACM SIGPLAN Symposium on Principles and Practices of Parallel Programming$91981504 997 $aUNINA