000  04359nam a22005535i 4500  

001  9780387927107  
003  DEHe213  
005  20180115171428.0  
007  cr nn 008mamaa  
008  130821s2010 xxu s  0eng d  
020 
_a9780387927107 _99780387927107 

024  7 
_a10.1007/9780387927107 _2doi 

050  4  _aQA276280  
072  7 
_aPBT _2bicssc 

072  7 
_aMAT029000 _2bisacsh 

082  0  4 
_a519.5 _223 
100  1 
_aThas, Olivier. _eauthor. 

245  1  0 
_aComparing Distributions _h[electronic resource] / _cby Olivier Thas. 
264  1 
_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2010. 

300 
_aXVI, 354 p. _bonline resource. 

336 
_atext _btxt _2rdacontent 

337 
_acomputer _bc _2rdamedia 

338 
_aonline resource _bcr _2rdacarrier 

347 
_atext file _bPDF _2rda 

490  1 
_aSpringer Series in Statistics, _x01727397 

505  0  _aOneSample Problems  Preliminaries (Building Blocks)  Graphical Tools  Smooth Tests  Methods Based on the Empirical Distribution Function  TwoSample and KSample Problems  Preliminaries (Building Blocks)  Graphical Tools  Some Important TwoSample Tests  Smooth Tests  Methods Based on the Empirical Distribution Function  Two Final Methods and Some Final Thoughts.  
520  _aComparing Distributions refers to the statistical data analysis that encompasses the traditional goodnessoffit testing. Whereas the latter includes only formal statistical hypothesis tests for the onesample and the Ksample problems, this book presents a more general and informative treatment by also considering graphical and estimation methods. A procedure is said to be informative when it provides information on the reason for rejecting the null hypothesis. Despite the historically seemingly different development of methods, this book emphasises the similarities between the methods by linking them to a common theory backbone. This book consists of two parts. In the first part statistical methods for the onesample problem are discussed. The second part of the book treats the Ksample problem. Many sections of this second part of the book may be of interest to every statistician who is involved in comparative studies. The book gives a selfcontained theoretical treatment of a wide range of goodnessoffit methods, including graphical methods, hypothesis tests, model selection and density estimation. It relies on parametric, semiparametric and nonparametric theory, which is kept at an intermediate level; the intuition and heuristics behind the methods are usually provided as well. The book contains many data examples that are analysed with the cd Rpackage that is written by the author. All examples include the Rcode. Because many methods described in this book belong to the basic toolbox of almost every statistician, the book should be of interest to a wide audience. In particular, the book may be useful for researchers, graduate students and PhD students who need a starting point for doing research in the area of goodnessoffit testing. Practitioners and applied statisticians may also be interested because of the many examples, the Rcode and the stress on the informative nature of the procedures. Olivier Thas is Associate Professor of Biostatistics at Ghent University. He has published methodological papers on goodnessoffit testing, but he has also published more applied work in the areas of environmental statistics and genomics.  
650  0  _aStatistics.  
650  0  _aOperations research.  
650  0  _aDecision making.  
650  0  _aData mining.  
650  0  _aBiostatistics.  
650  0  _aProbabilities.  
650  0  _aSocial sciences.  
650  1  4  _aStatistics. 
650  2  4  _aStatistics, general. 
650  2  4  _aProbability Theory and Stochastic Processes. 
650  2  4  _aMethodology of the Social Sciences. 
650  2  4  _aBiostatistics. 
650  2  4  _aData Mining and Knowledge Discovery. 
650  2  4  _aOperation Research/Decision Theory. 
710  2  _aSpringerLink (Online service)  
773  0  _tSpringer eBooks  
776  0  8 
_iPrinted edition: _z9780387927091 
830  0 
_aSpringer Series in Statistics, _x01727397 

856  4  0  _uhttp://dx.doi.org/10.1007/9780387927107 
912  _aZDB2SMA  
999 
_c369831 _d369831 