LEADER 03686nam 2200589Ia 450 001 9910739446503321 005 20200520144314.0 010 $a3-642-34919-6 024 7 $a10.1007/978-3-642-34919-5 035 $a(CKB)2550000001045637 035 $a(EBL)1206067 035 $a(SSID)ssj0000878439 035 $a(PQKBManifestationID)11532598 035 $a(PQKBTitleCode)TC0000878439 035 $a(PQKBWorkID)10814045 035 $a(PQKB)11164185 035 $a(DE-He213)978-3-642-34919-5 035 $a(MiAaPQ)EBC1206067 035 $a(PPN)169138410 035 $a(EXLCZ)992550000001045637 100 $a20130320d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAllocating taxing powers within the European Union /$fIsabelle Richelle, Wolfgang Schon, Edoardo Traversa (editors) 205 $a1st ed. 2013. 210 $aHeidelberg ;$aNew York $cSpringer$dc2013 215 $a1 online resource (220 p.) 225 1 $aMPI studies in tax law and public finance,$x2196-0011 ;$vv.2 300 $aDescription based upon print version of record. 311 $a3-642-43637-4 311 $a3-642-34918-8 327 $aA.G. Prats: Revisiting "Schumacker": Source, Residence and Citizenshipin the ECJ Case Low on Direct Taxation -- M. Greggi: Revisiting "Schumacker": The Role of Limited Tax Liability in EU Law -- D. Gutmann: How to avoid Double Taxation in the European Union?- W. Schön: Transfer Pricing, the Arm's Length Standard and European Union Law -- I. Richelle: Cross-Border Loss Compensation: State and Critique of the Judicature -- T. Hackemann: Group Taxation in the European Union -- V.R. Almendral: Tax Avoidance, the "Balanced Allocation of Taxing Powers" and the Arm's Length Standard: and odd Threesome in Need of Clarification -- E. Traversa, B. Vintras: The Territoriality of Tax Incentives within the Single Market -- E. Reimer: Taxation - an Area without Mutual Recognition? 330 $aThe contributions to this volume try to overcome the traditional approach of the judicature of the European Court of Justice regarding the application of the fundamental freedoms in direct taxation that is largely built on a non-discrimination test. In this volume, outstanding authors cover various aspects of the national and international tax order when European law meets domestic taxation. This includes testing traditional pillars of income taxation ? ability-to-pay, source and residence, abuse of law, arm?s length standard ? with respect to their place in the emerging European tax order as well as substantial matters of co-existence between different tax systems that are not covered by the non-discrimination approach such as mutual recognition, cross-border loss compensation or avoidance of double taxation. The overarching goal is to flesh out the extent to which a substantive ?allocation of taxing powers? within the European Union is on its way to a convincing overall framework and to stretch the discussion ?beyond discrimination?.  . 410 0$aMPI studies in tax law and public finance ;$vv.2. 606 $aTaxation$zEuropean Union countries 606 $aFinance, Public$zEuropean Union countries 615 0$aTaxation 615 0$aFinance, Public 676 $a343.2404 701 $aRichelle$b Isabelle$01758250 701 $aSchon$b Wolfgang$cProf.$01758251 701 $aTraversa$b Edoardo$01758252 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739446503321 996 $aAllocating taxing powers within the European Union$94196397 997 $aUNINA LEADER 04980nam 22007815 450 001 9910157379203321 005 20220627143544.0 010 $a9781484225141 010 $a1484225147 024 7 $a10.1007/978-1-4842-2514-1 035 $a(CKB)3710000001000980 035 $a(DE-He213)978-1-4842-2514-1 035 $a(MiAaPQ)EBC4774045 035 $a(CaSebORM)9781484225141 035 $a(PPN)197452892 035 $a(OCoLC)1076490733 035 $a(OCoLC)on1076490733 035 $a(EXLCZ)993710000001000980 100 $a20161227d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBusiness Analytics Using R - A Practical Approach /$fby Umesh R Hodeghatta, Umesha Nayak 205 $a1st ed. 2017. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2017. 215 $a1 online resource (XVII, 280 p. 278 illus.) 311 08$a9781484225134 311 08$a1484225139 320 $aIncludes bibliographical references and index. 327 $aOverview of business analytics -- Introduction to R -- R for data analysis -- Introduction to descriptive analytics -- Business analytics process and data exploration -- Supervised machine learning : classification -- Unsupervised machine learning -- Simple linear regression -- Multiple linear regression -- Logistic regression -- Big data analysis : introduction and future trends. 330 $aLearn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. You will: ? Write R programs to handle data ? Build analytical models and draw useful inferences from them ? Discover the basic concepts of data mining and machine learning ? Carry out predictive modeling ? Define a business issue as an analytical problem. 606 $aBig data 606 $aComputer programming 606 $aProgramming languages (Electronic computers) 606 $aData mining 606 $aInformation storage and retrieval 606 $aMathematical statistics 606 $aR (Computer program language) 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 615 0$aBig data. 615 0$aComputer programming. 615 0$aProgramming languages (Electronic computers) 615 0$aData mining. 615 0$aInformation storage and retrieval. 615 0$aMathematical statistics. 615 0$aR (Computer program language) 615 14$aBig Data. 615 24$aProgramming Techniques. 615 24$aProgramming Languages, Compilers, Interpreters. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 24$aProbability and Statistics in Computer Science. 676 $a658.054 700 $aHodeghatta$b Umesh R$4aut$4http://id.loc.gov/vocabulary/relators/aut$0900654 702 $aNayak$b Umesha$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910157379203321 996 $aBusiness Analytics Using R - A Practical Approach$92012801 997 $aUNINA