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分类依据与纠错

AI 根据所提供的论文材料归类:摘要研究一般列联表的小频数估计,未描述离散选择行为模型或其他所列经济计量方向。

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关注这篇论文的作者
I. J. Good

列联表中小频数的估计

研究问题
在含异质性的大型列联表中,若小单元格、近乎正交的行或缺失单元格使行列合并不可靠,如何估计对应的总体频率?
方法与思路
作者对单元格关联因子设定初始分布,分别考虑其对数为正态分布及因子服从 Pearson III 型分布,并借助后效函数表达更新后的期望与方差。
材料陈述的贡献
摘要称,方法提供总体频率及其对数的期望、方差表达,并汇集后效函数及若干推广的性质,为进一步制表提供依据。
阅读关注点
应核对关联因子的定义、初始分布与观测信息如何结合、缺失单元格的处理,以及两种分布假设在准确性和计算便利性上的差别。
阅读边界
仅依据所给 Crossref 摘要;观测模型、缺失机制和初始分布选择未说明,不能确认该方法在任意稀疏列联表中均有效。

原文摘要

Summary Suppose that we require estimates of the population frequencies corresponding to small entries in a large pure contingency table containing heterogeneity. It would be natural to lump rows or columns together with weights depending on the correlation coefficients. But if the rows, say, are nearly orthogonal, no lumping method will be reasonable. Moreover, if many of the entries in the table are missing, a lumping method may fail. A method is given here (which it may be possible to combine with a lumping method when appropriate) that depends on an assumption for the initial distribution of the “association factor” in each cell, i.e. the ratio of the population frequency in the cell to the product of the population frequencies of its row and column. If the logarithm of the association factor is assumed to have, initially, a normal distribution, then the final expectations and variances of the population frequencies and of their logarithms can be expressed in terms of the “after-effect” function which was originally tabulated by K. W. Wagner for an electrodynamical application. Instead of a log-normal distribution, a Pearson Type III distribution may be assumed for the association factor. This assumption may be less accurate but is easier to handle. The paper concludes with a list of properties of the after-effect function (and of some of its generalizations) that may be useful for further tabulation.

笔记与分组
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