LEADER 05415nam 22006614a 450 001 9910143420503321 005 20170809162043.0 010 $a1-280-28698-9 010 $a9786610286980 010 $a0-470-36261-8 010 $a0-471-77145-7 010 $a0-471-77144-9 035 $a(CKB)1000000000354973 035 $a(EBL)242881 035 $a(OCoLC)71791637 035 $a(SSID)ssj0000182790 035 $a(PQKBManifestationID)11170399 035 $a(PQKBTitleCode)TC0000182790 035 $a(PQKBWorkID)10172849 035 $a(PQKB)10124791 035 $a(MiAaPQ)EBC242881 035 $a(EXLCZ)991000000000354973 100 $a20050622d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIntroduction to nonparametric regression$b[electronic resource] /$fKunio Takezawa 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (566 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-74583-9 320 $aIncludes bibliographical references (p. 529-531) and index. 327 $aINTRODUCTION TO NONPARAMETRIC REGRESSION; CONTENTS; Preface; Acknowledgments; 1 Exordium; 1.1 Introduction; 1.2 Are the moving average and Fourier series sufficiently useful?; 1.3 Is a histogram or normal distribution sufficiently powerful?; 1.4 Is interpolation sufficiently powerful?; 1.5 Should we use a descriptive equation?; 1.6 Parametric regression and nonparametric regression; 2 Smoothing for data with an equispaced predictor; 2.1 Introduction; 2.2 Moving average and binomial filter; 2.3 Hat matrix; 2.4 Local linear regression; 2.5 Smoothing spline 327 $a2.6 Analysis on eigenvalue of hat matrix2.7 Examples of S-Plus object; References; Problems; 3 Nonparametric regression for one-dimensional predictor; 3.1 Introduction; 3.2 Trade-off between bias and variance; 3.3 Index to select beneficial regression equations; 3.4 Nadaraya-Watson estimator; 3.5 Local polynomial regression; 3.6 Natural spline and smoothing spline; 3.7 LOESS; 3.8 Supersmoother; 3.9 LOWESS; 3.10 Examples of S-Plus object; References; Problems; 4 Multidimensional smoothing; 4.1 Introduction; 4.2 Local polynomial regression for multidimensional predictor 327 $a4.3 Thin plate smoothing splines4.4 LOESS and LOWESS with plural predictors; 4.5 Kriging; 4.6 Additive model; 4.7 ACE; 4.8 Projection pursuit regression; 4.9 Examples of S-Plus object; References; Problems; 5 Nonparametric regression with predictors represented as distributions; 5.1 Introduction; 5.2 Use of distributions as predictors; 5.3 Nonparametric DVR method; 5.4 Form of nonparametric regression with predictors represented as distributions; 5.5 Examples of S-Plus object; References; Problems; 6 Smoothing of histograms and nonparametric probability density functions; 6.1 Introduction 327 $a6.2 Histogram6.3 Smoothing a histogram; 6.4 Nonparametnc probability density function; 6.5 Examples of S-Plus object; References; Problems; 7 Pattern recognition; 7.1 Introduction; 7.2 Bayes' decision rule; 7.3 Linear discriminant rule and quadratic discriminant rule; 7.4 Classification using nonparametric probability density function; 7.5 Logistic regression; 7.6 Neural networks; 7.7 Tree-based model; 7.8 k-nearest-neighbor classifier; 7.9 Nonparametric regression based on the least squares; 7.10 Transformation of feature vectors; 7.11 Examples of S-Plus object; References; Problems 327 $aAppendix A: Creation and applications of B-spline basesA.1 Introduction; A.2 Method to create B-spline basis; A.3 Natural spline created by B-spline; A.4 Application to smoothing spline; A.5 Examples of S-Plus object; References; Appendix B: R objects; B.1 Introduction; B.2 Transformation of S-Plus objects in Chapter 2; B.3 Transformation of S-Plus objects in Chapter 3; B.4 Transformation of S-Plus objects in Chapter 4; B.5 Transformation of S-Plus objects in Chapter 5; B.6 Transformation of S-Plus objects in Chapter 6; B.7 Transformation of S-Plus objects in Chapter 7 327 $aB.8 Transformation of S-Plus objects in Appendix A 330 $aAn easy-to-grasp introduction to nonparametric regressionThis book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features:* Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods* Statistical techniques accompanied by clear numerical examples that fur 410 0$aWiley series in probability and statistics. 606 $aRegression analysis$vTextbooks 606 $aNonparametric statistics$vTextbooks 608 $aElectronic books. 615 0$aRegression analysis 615 0$aNonparametric statistics 676 $a519.5/36 676 $a519.536 700 $aTakezawa$b Kunio$f1959-$0520704 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143420503321 996 $aIntroduction to nonparametric regression$92019499 997 $aUNINA LEADER 03148oam 2200433K 450 001 9910793599403321 005 20190503073449.0 010 $a0-262-34810-1 035 $a(CKB)4100000007745160 035 $a(MiAaPQ)EBC5723092 035 $a(OCoLC)1083097418 035 $a(OCoLC-P)1083097418 035 $a(MaCbMITP)11819 035 $a(PPN)236069780 035 $a(EXLCZ)994100000007745160 100 $a20190121d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Jean Freeman Gallery does not exist /$fChristopher Howard 210 1$aCambridge :$cMIT Press,$d2019. 215 $a1 online resource (413 pages) 311 $a0-262-03846-3 320 $aIncludes bibliographical references and index. 330 $aAn examination of a 1970s Conceptual art project--advertisements for fictional shows by fictional artists in a fictional gallery--that hoodwinked the New York art world. From the summer of 1970 to March 1971, advertisements appeared in four leading art magazines-- Artforum , Art in America , Arts Magazine, and ARTnews --for a group show and six solo exhibitions at the Jean Freeman Gallery at 26 West Fifty-Seventh Street, in the heart of Manhattan's gallery district. As gallery goers soon discovered, this address did not exist -- the street numbers went from 16 to 20 to 24 to 28--and neither did the art supposedly exhibited there. The ads were promoting fictional shows by fictional artists in a fictional gallery. The scheme, eventually exposed by a New York Times reporter, was concocted by the artist Terry Fugate-Wilcox as both work of art and critique of the art world. In this book, Christopher Howard brings this forgotten Conceptual art project back into view. Howard demonstrates that Fugate-Wilcox's project was an exceptionally clever embodiment of many important aspects of Conceptualism, incisively synthesizing the major aesthetic issues of its time--documentation and dematerialization, serialism and process, text and image, publishing and publicity. He puts the Jean Freeman Gallery in the context of other magazine-based work by Mel Bochner, Judy Chicago, Yoko Ono, and Ed Ruscha, and compares the fictional artists' projects with actual Earthworks by Walter De Maria, Peter Hutchinson, Dennis Oppenheim, and more. Despite the deadpan perfection of the Jean Freeman Gallery project, the art establishment marginalized its creator, and the project itself was virtually erased from art history. Howard corrects these omissions, drawing on deep archival research, personal interviews, and investigation of fine-printed clues to shed new light on a New York art world mystery. 606 $aConceptual art$zUnited States 606 $aArt$xDocumentation 615 0$aConceptual art 615 0$aArt$xDocumentation. 676 $a700.973 700 $aHoward$b Christopher$f1974-$01547280 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910793599403321 996 $aThe Jean Freeman Gallery does not exist$93803539 997 $aUNINA