Dispersion of Categorical Variables and Penalty Functions: Derivation, Estimation, and Comparability
作者:
Zvi Gilula,
ShelbyJ. Haberman,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 432
页码: 1447-1452
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476651
出版商: Taylor & Francis Group
关键词: Concentration;Entropy;Goodman–Kruskal measures;Majorization;Stochastic order
数据来源: Taylor
摘要:
Measures of dispersion for categorical random variables based on penalty functions play a central role in establishing relevant measures of association between such variables. The literature concerning these measures provides little systematic treatment of such aspects of these measures as comparability, efficient estimation, and large-sample properties. This article provides a systematic and rigorous construction of dispersion measures based on penalty functions. Efficient estimation procedures and asymptotic properties of estimates are examined. Conditions from majorization theory that ensure a meaningful comparability of dispersion measures based on penalty functions are discussed. A large class of familiar dispersion measures is then given a new interpretation using these conditions.
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