LEADER 00926nam0-2200301---450- 001 990009189280403321 005 20100525113600.0 035 $a000918928 035 $aFED01000918928 035 $a(Aleph)000918928FED01 035 $a000918928 100 $a20100525d1987----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $aa-------001yy 200 1 $aNapoli donna$etrentasette donne$finterviste: Giuliana Gargiulo$gfotografie: Augusto De Luca$gprefazione di Lina Wertmüller 210 $a[S. l.]$cCentro Il diaframma$aMilano$cEditphoto$dstampa 1987 215 $a[81] c.$cill.$d33 cm 610 0 $aDonne napoletane$aInterviste 700 1$aGargiulo,$bGiuliana$0315703 701 1$aDe Luca,$bAugusto$0507959 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990009189280403321 952 $a30.285$b8063$fDARST 959 $aDARST 996 $aNapoli donna$9775869 997 $aUNINA LEADER 01243nam--2200397---450- 001 990000473650203316 005 20050606181555.0 035 $a0047365 035 $aUSA010047365 035 $a(ALEPH)000047365USA01 035 $a0047365 100 $a20010528d1989----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> buon messaggio seguendo Matteo$fa cura di Enzo Mandruzzato 210 $aPordenone$cBiblioteca dell'Immagine$d1989 215 $aXLV, 186 p.$d22 cm 225 2 $aIl soggetto & la scienza 300 $aTesto orig. a fronte 410 $12001$aIl soggetto & la scienza 461 1$1001-------$12001 500 11$aBibbia. Nuovo Testamento. Vangelo secondo Matteo$955631 676 $a226.2 702 1$aMANDRUZZATO,$bEnzo 801 0$aIT$bsalbc$gISBD 912 $a990000473650203316 951 $aII.1 Coll.61/ 2(IV A Coll. 252/2)$b98532 LM$cIV A Coll. 959 $aBK 969 $aUMA 979 $aPATTY$b90$c20010528$lUSA01$h1154 979 $c20020403$lUSA01$h1656 979 $aPATRY$b90$c20040406$lUSA01$h1633 979 $aCOPAT2$b90$c20050606$lUSA01$h1815 996 $aBIBBIA. Nuovo Testamento. Vangelo secondo Matteo$955631 997 $aUNISA LEADER 03731nam 2200577 450 001 9910829984503321 005 20211009102710.0 010 $a1-119-41741-4 010 $a1-119-41740-6 010 $a1-119-41739-2 035 $a(CKB)4100000011788467 035 $a(MiAaPQ)EBC6508332 035 $a(Au-PeEL)EBL6508332 035 $a(OCoLC-P)1193558110 035 $a(CaSebORM)9781119417385 035 $a(OCoLC)1193558110 035 $a(EXLCZ)994100000011788467 100 $a20211009d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical learning for big dependent data /$fDaniel Pen?a, Ruey S. Tsay 205 $aFirst edition. 210 1$aHoboken, New Jersey :$cWiley,$d[2021] 210 4$dİ2021 215 $a1 online resource (563 pages) 225 1 $aWiley series in probability and statistics 311 18$a1-119-41738-4 327 $aIntroduction to big dependent data -- Linear univariate time series -- Analysis of multivariate time series -- Handling heterogeneity in many time series -- Clustering and classification of time series -- Dynamic factor models -- Forecasting with big dependent data -- Machine learning of big dependent data -- Spatio-temporal dependent data. 330 $a"This book presents methods useful for analyzing and understanding large data sets that are dynamically dependent. The book will begin with examples of multivariate dependent data and tools for presenting descriptive statistics of such data. It then introduces some useful statistical methods for univariate time series analysis emphasizing on statistical procedures for modeling and forecasting. Both linear and nonlinear models are discussed. Special attention is given to analysis of high-frequency dependent data. The second part of the book considers joint dependency, both contemporaneous and dynamical dependence, among multiple series of dependent data. Special attention will be given to graphical methods for large data, to handling heterogeneity in time series (such as outliers, missing values, and changes in the covariance matrices), and to time-varying parameters for multivariate time series. The third part of the book is devoted to analysis of high-dimensional dependent data. The focus is on topics that are useful when the number of time series is large. The selected topics include clustering time series, high-dimensional linear regression for dependent data and its applications, and reducing the dimension with dynamic principal components and factor models. Throughout the book, advantages and disadvantages of the methods discussed are given and real examples are used in demonstration. The book will be of interest to graduate students, researchers, and practitioners in business, economics, engineering, and science who are interested in statistical methods for analyzing big dependent data and forecasting"--$cProvided by publisher. 410 0$aWiley series in probability and statistics. 606 $aBig data$xMathematics 606 $aTime-series analysis 606 $aData mining$xStatistical methods 606 $aForecasting$xStatistical methods 615 0$aBig data$xMathematics. 615 0$aTime-series analysis. 615 0$aData mining$xStatistical methods. 615 0$aForecasting$xStatistical methods. 676 $a005.7 700 $aPen?a$b Daniel$f1948-$0614022 702 $aTsay$b Ruey S.$f1951- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829984503321 996 $aStatistical learning for big dependent data$94014473 997 $aUNINA