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The Gini Methodology: A Primer on a Statistical Methodology
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Catalogue Information
Field name
Details
Dewey Class
519.5
Title
The Gini Methodology ([Ebook]) : A Primer on a Statistical Methodology / by Shlomo Yitzhaki, Edna Schechtman.
Author
Yitzhaki, Shlomo
Added Personal Name
Schechtman, Edna
Other name(s)
SpringerLink (Online service)
Publication
New York, NY : Springer
, 2013.
Physical Details
XVI, 548 pages:. 43 illus. : online resource.
Series
Springer Series in Statistics
0172-7397 ; ; 272
ISBN
9781461447207
Summary Note
Gini'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. Â With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.:
Contents note
Introduction -- 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 .
System details note
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site
http://dx.doi.org/10.1007/978-1-4614-4720-7
Links to Related Works
Subject References:
Econometrics
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Economics
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Financial Economics
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Mathematical Statistics
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Statistics
.
Statistics for Business/Economics/Mathematical Finance/Insurance
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Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
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Authors:
author
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Schechtman, Edna
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Yitzhaki, Shlomo
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Corporate Authors:
SpringerLink (Online service)
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Series:
Springer Series in Statistics
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Classification:
519.5
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519.5 (DDC 23)
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