002743150 001__ 2743150
002743150 003__ SzGeCERN
002743150 005__ 20201104150427.0
002743150 0247_ $$2DOI$$a10.1142/9781860948985_0040
002743150 0248_ $$aoai:inspirehep.net:706592$$pcerncds:CERN$$qINSPIRE:HEP$$qCERN$$qForCDS
002743150 035__ $$9http://old.inspirehep.net/oai2d$$aoai:inspirehep.net:706592$$d2020-10-29T12:27:51Z$$h2020-10-31T05:00:37Z$$mmarcxml
002743150 035__ $$9Inspire$$a706592
002743150 041__ $$aeng
002743150 100__ $$aMascialino, B$$uINFN, Genoa
002743150 245__ $$aAn update on the goodness-of-fit statistical toolkit
002743150 260__ $$c2006
002743150 269__ $$c2005-09
002743150 300__ $$a3 p
002743150 520__ $$aThe present project aims to develop an open-source and object-oriented software Toolkit for statistical data analysis. Its statistical testing component (the Goodness-of-Fit Statistical Toolkit) contains a variety of one dimensional Goodness-of-Fit tests, from Chi-squared to Kolmogorov-Smirnov, to less known, but generally much more powerful tests such as Anderson-Darling, Cramèr-von Mises, Kuiper, Watson, … The GoF Statistical Toolkit is open-source and downloadable from the web, with its user and software documentation. The component-based design allowed an extension of the GoF Statistical Toolkit: less known, but generally more powerful GoF tests based on EDF-statistics have been recently added to the toolkit. A much more complete variety of GoF inferences is now offered the user, and “standard” GoF tests have been complemented by more “exotic” ones. The weighted formulations of some GoF tests (Kolmogorov-Smirnov and Cramèr-von Mises) have been implemented. Approximations of the distribution of some of the existing GoF tests to the Chi-squared one (Kolmogorov-Smirnov, Cramèr-von Mises, and Watson approximations) are now available in the GoF Statistical Toolkit. Moreover, a layer for user input from ROOT objects has been easily added recently, thanks to the component-based architecture. We present the recent improvements and extensions of the GoF Statistical Toolkit, describing the new statistics methods implemented, and an outlook towards future developments.
002743150 542__ $$f© 2006 by Imperial College Press
002743150 65017 $$2SzGeCERN$$aComputing and Computers
002743150 690C_ $$aCERN
002743150 700__ $$aPia, M G$$uINFN, Genoa
002743150 700__ $$aPfeiffer, A$$uCERN
002743150 700__ $$aRibon, A$$uCERN
002743150 700__ $$aViarengo, P$$uUnlisted
002743150 773__ $$0728115$$c190-192$$wC05-09-12$$y2006
002743150 960__ $$a13
002743150 962__ $$b780058$$k190-192$$noxford20050912
002743150 980__ $$aARTICLE
002743150 980__ $$aConferencePaper
002743150 980__ $$aBookChapter