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001 978-1-4614-4720-7
003 DE-He213
005 20140220082816.0
007 cr nn 008mamaa
008 130607s2013 xxu| s |||| 0|eng d
020 _a9781461447207
_9978-1-4614-4720-7
024 7 _a10.1007/978-1-4614-4720-7
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aYitzhaki, Shlomo.
_eauthor.
245 1 4 _aThe Gini Methodology
_h[electronic resource] :
_bA Primer on a Statistical Methodology /
_cby Shlomo Yitzhaki, Edna Schechtman.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXVI, 548 p. 43 illus.
_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,
_x0172-7397 ;
_v272
505 0 _aIntroduction -- More Than a Dozen Alternative Ways of Spelling Gini -- The Gini equivalents of the covariance, the correlation and the regression coefficient -- Decompositions of the GMD -- The Lorenz curve and the concentration curve -- The extended Gini family of measures -- Gini Simple Regressions -- Multiple Regressions -- Inference on Gini-based parameters -estimation -- Inference on Gini-based parameters -testing -- Inference on Lorenz and on Concentration curves -- Introduction to applications -- Social welfare, relative deprivation and the Gini coefficient -- Policy Analysis.-  Policy Analysis Using the Decomposition of the Gini by non-marginal analysis.- Incorporating poverty in Policy Analysis - the Marginal Analysis case -- Introduction to applications of the GMD and the Lorenz curve in finance -- The mean-Gini portfolio and the pricing of capital assets -- Applications of Gini methodology in regression analysis -- Gini's multiple regressions: two approaches and their interaction -- Mixed OLS, Gini and extended Gini regressions.-  An application in statistics - ANOGI -- Suggestions for further research   .
520 _aGini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers. This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology. Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model.  With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.
650 0 _aStatistics.
650 0 _aMathematical statistics.
650 0 _aEconomics
_xStatistics.
650 0 _aEconometrics.
650 0 _aFinance.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aStatistics, general.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aFinancial Economics.
650 2 4 _aEconometrics.
650 2 4 _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
700 1 _aSchechtman, Edna.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461447191
830 0 _aSpringer Series in Statistics,
_x0172-7397 ;
_v272
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-4720-7
912 _aZDB-2-SMA
999 _c95209
_d95209