RELATIONSHIP BETWEEN LOGARITHMIC SERIES MODEL AND OTHER SUPERPOPULATION MODELS USEFUL FOR MICRODATA DISCLOSURE RISK ASSESSMENT
RELATIONSHIP BETWEEN LOGARITHMIC SERIES MODEL AND OTHER SUPERPOPULATION MODELS USEFUL FOR MICRODATA DISCLOSURE RISK ASSESSMENT
复制标题
对数级数模型与其他可用于微数据披露风险评估的超总体模型之间的关系
DOI：
10.14490/jjss1995.28.125
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
A. Takemura
中科院分区：
文献类型：
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作者：
Nobuaki Hoshino;A. Takemura
Fisher的对数级数模型(Fisher et al.(1943))是统计生态学中的经典模型。在本文中，我们证明了该模型是连接Takemura(1997)中讨论的三个模型的关键模型，即Poisson-Gamma模型(伯利恒等人)。(1990))、Dirichlet多项式模型(Takemura(1997))和Ewens模型(Ewens(1990))。这一联系为将现有的统计生态学技术应用于微数据披露风险评估问题提供了可能性。
Fisher's logarithmic series model (Fisher et al. (1943)) is a classical model in statistical ecology. In this paper we show that this model is a key model linking three models discussed in Takemura (1997), i.e., Poisson-gamma model (Bethlehem et al. (1990)), Dirichlet-multinomial model (Takemura (1997)), and Ewens model (Ewens (1990)). This connection opens up the possibility of applying existing techniques of statistical ecology to the problem of microdata disclosure risk assessment.