01015nam0 22002771i 450 VAN0002901320240806100339.85688-13-23871-120041129d2002 |0itac50 baitaIT|||| |||||ˆLa ‰truffaaspetti penali, civili, processualiUmberto LucarelliPadovaCedam2002XIV, 418 p.24 cm.001VAN000089792001 Diritto italiano210 PadovaCEDAM11TruffaVANC012961FIPadovaVANL000007LucarelliUmbertoVANV023973268009CEDAM <editore>VANV111515650ITSOL20250131RICABIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZAIT-CE0105VAN00VAN00029013BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA00CONS XIV.Eb.166 00 25061 20041129 Truffa677053UNICAMPANIA02953nam 22006135 450 991030025020332120250408085140.03-662-48344-010.1007/978-3-662-48344-2(CKB)3710000000476913(EBL)4178938(SSID)ssj0001585490(PQKBManifestationID)16264597(PQKBTitleCode)TC0001585490(PQKBWorkID)14864339(PQKB)11579975(DE-He213)978-3-662-48344-2(MiAaPQ)EBC4178938(PPN)190522763(EXLCZ)99371000000047691320150915d2015 u| 0engur|n|---|||||txtccrRankings and Preferences New Results in Weighted Correlation and Weighted Principal Component Analysis with Applications /by Joaquim Pinto da Costa1st ed. 2015.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2015.1 online resource (95 p.)SpringerBriefs in Statistics,2191-5458Description based upon print version of record.3-662-48343-2 Includes bibliographical references.Introduction -- The Weighted Rank Correlation Coefficient rW -- The Weighted Rank Correlation Coefficient rW2 -- A Weighted Principal Component Analysis, WPCA1: Application to Gene Expression Data -- A Weighted Principal Component Analysis (WPCA2) for Time Series Data -- Weighted Clustering of Time Series -- Appendix -- References.This book examines in detail the correlation, more precisely the weighted correlation, and applications involving rankings. A general application is the evaluation of methods to predict rankings. Others involve rankings representing human preferences to infer user preferences; the use of weighted correlation with microarray data and those in the domain of time series. In this book we present new weighted correlation coefficients and new methods of weighted principal component analysis. We also introduce new methods of dimension reduction and clustering for time series data, and describe some theoretical results on the weighted correlation coefficients in separate sections.SpringerBriefs in Statistics,2191-5458StatisticsBiometryStatistical Theory and MethodsBiostatisticsStatistics.Biometry.Statistical Theory and Methods.Biostatistics.519.537Pinto da Costa Joaquimauthttp://id.loc.gov/vocabulary/relators/aut755704MiAaPQMiAaPQMiAaPQBOOK9910300250203321Rankings and preferences1522865UNINA