LEADER 00803nam a2200229 i 4500 001 991002921649707536 005 20021022144039.0 008 960610s1959 it ||| | ita 035 $ab11728851-39ule_inst 035 $aLE021FD222944$9ExL 040 $aDip. SSSC$bita 100 1 $aMagliulo, Gennaro$036887 245 10$aEduardo De Filippo /$cGennaro Magliulo 260 $aBologna :$bCappelli,$c1959 300 $a88 p., 8 c. di tav. ;$c19 cm. 650 4$aDe Filippo, Eduardo 907 $a.b11728851$b21-09-06$c24-10-02 912 $a991002921649707536 945 $aLE021FD TI18B29$g1$iLE021FD-2381$lle023$nFondo D'Amico$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i11968795$z24-10-02 996 $aEduardo De Filippo$9202377 997 $aUNISALENTO 998 $ale021$b10-06-96$cm$da $e-$fita$git $h0$i1 LEADER 03612nam 2200541 450 001 9910271040703321 005 20231214053711.0 010 $a1-118-89050-7 010 $a1-118-89037-X 035 $a(CKB)4330000000007389 035 \\$a(Safari)9781118456057 035 $a(OCoLC)1031279328 035 $a(Au-PeEL)EBL5117030 035 $a(CaPaEBR)ebr11462463 035 $a(OCoLC)1009244078 035 $a(CaSebORM)9781118456057 035 $a(MiAaPQ)EBC5117030 035 $a(EXLCZ)994330000000007389 100 $a20171124h20182018 uy 0 101 0 $aeng 135 $aurunu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKernel smoothing $eprinciples, methods and applications /$fSucharita Ghosh 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d2018. 210 4$dİ2018 215 $a1 online resource (1 volume) $cillustrations 225 1 $aTHEi Wiley ebooks 311 $a1-118-45605-X 320 $aIncludes bibliographical references and indexes. 327 $aDensity estimation -- Nonparametric regression -- Trend estimation -- Semiparametric regression -- Surface estimation. 330 $aComprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of contexts, considering independent and correlated data e.g. with short-memory and long-memory correlations, as well as non-Gaussian data that are transformations of latent Gaussian processes. These types of data occur in many fields of research, e.g. the natural and the environmental sciences, and others. Nonparametric density estimation, nonparametric and semiparametric regression, trend and surface estimation in particular for time series and spatial data and other topics such as rapid change points, robustness etc. are introduced alongside a study of their theoretical properties and optimality issues, such as consistency and bandwidth selection. Addressing a variety of topics, Kernel Smoothing: Principles, Methods and Applications offers a user-friendly presentation of the mathematical content so that the reader can directly implement the formulas using any appropriate software. The overall aim of the book is to describe the methods and their theoretical backgrounds, while maintaining an analytically simple approach and including motivating examples?making it extremely useful in many sciences such as geophysics, climate research, forestry, ecology, and other natural and life sciences, as well as in finance, sociology, and engineering. A simple and analytical description of kernel smoothing methods in various contexts Presents the basics as well as new developments Includes simulated and real data examples Kernel Smoothing: Principles, Methods and Applications is a textbook for senior undergraduate and graduate students in statistics, as well as a reference book for applied statisticians and advanced researchers. 410 0$aTHEi Wiley ebooks. 606 $aSmoothing (Statistics) 606 $aKernel functions 615 0$aSmoothing (Statistics) 615 0$aKernel functions. 676 $a511/.42 700 $aGhosh$b S$g(Sucharita),$061429 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910271040703321 996 $aKernel smoothing$92071064 997 $aUNINA