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Book ChapterDOI

Measures of Association for Cross Classifications III: Approximate Sampling Theory

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TLDR
In this paper, the authors derived large sample normal distributions with their associated standard errors for various measures of association and various methods of sampling and explained how the large sample normality may be used to test hypotheses about the measures and about differences between them, and to construct corresponding confidence intervals.
Abstract
The population measures of association for cross classifications, discussed in the authors' prior publications, have sample analogues that are approximately normally distributed for large samples. (Some qualifications and restrictions are necessary.) These large sample normal distributions with their associated standard errors, are derived for various measures of association and various methods of sampling. It is explained how the large sample normality may be used to test hypotheses about the measures and about differences between them, and to construct corresponding confidence intervals. Numerical results are given about the adequacy of the large sample normal approximations. In order to facilitate extension of the large sample results to other measures of association, and to other modes of sampling, than those treated here, the basic manipulative tools of large sample theory are explained and illustrated.

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Citations
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Journal ArticleDOI

Gene order data from a model amphibian (Ambystoma): new perspectives on vertebrate genome structure and evolution

TL;DR: This first comparative study of vertebrate genome structure that includes a representative amphibian is reported, suggesting relatively constant interchromosomal rearrangement rates from the euteleost ancestor to the origin of mammals and illustrating the utility of amphibian mapping data in establishing ancestral amniote and tetrapod gene orders.

An Experimental Test

TL;DR: In this article, a series of experiments were conducted to test whether individuals partitioned into two chambers do in fact behave as the formal theory of bicameralism predicts, and the results showed that the outcome chosen under a given partition is almost always in the core for that partition, and that a change in the partition has a statistically significant impact on the choice of outcome.
Journal ArticleDOI

Stochastic process and archaeological mechanism in spatial analysis

TL;DR: Statistical techniques for the study of spatial patterns (“spatial analysis”) are reviewed, and their foundations are examined so that archaeologists can more readily judge how appropriate they are for dealing with particular problems.
Journal ArticleDOI

Symbolic or not-so-symbolic wounds: The behavioral ecology of human scarification.

TL;DR: In this article, the authors analyzed scarification from the perspective of four competing hypotheses: (1) a rite of passage, (2) a hardening/trauma procedure, (3) a nonadaptive sexually selected character, or (4) an adaptive pathogen-driven sexually selected characters.
References
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Book

Measures of association for cross classifications

TL;DR: In this article, a number of alternative measures are considered, almost all based upon a probabilistic model for activity to which the cross-classification may typically lead, and only the case in which the population is completely known is considered, so no question of sampling or measurement error appears.
Book ChapterDOI

A Class of Statistics with Asymptotically Normal Distribution

TL;DR: In this article, the authors considered the problem of estimating a U-statistic of the population characteristic of a regular functional function, where the sum ∑″ is extended over all permutations (α 1, α m ) of different integers, 1 α≤ (αi≤ n, n).
Journal ArticleDOI

Ordinal Measures of Association

TL;DR: The three measures considered at length are the quadrant measure, Kendall's tau, and Spearman's rho as mentioned in this paper, with emphasis on the probabilistic and operational interpretations of their population values.
Journal ArticleDOI

The Central Limit Theorem for Dependent Random Variables

TL;DR: The central limit theorem has been extended to the case of dependent random variables by several authors (Bruns, Markoff, S. Bernstein, P. Levy, and Loeve) as mentioned in this paper.