数据模型

J. Leeuw
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引用次数: 71

摘要

我们指出,基于概率论的模型以及由此衍生的统计技术的适用性有限,至少在探索性多元情况下是如此。先验知识,如果可用,必须纳入分析,以产生更大的稳定性。但是,如果没有先验知识,就不能发明。这适用于模型的结构和复制框架方面。介绍了测量方法和稳定性分析方法。他们使用技术的概念作为数据分析的枢纽,而不是模型的概念。以同质性分析为例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Models for Data
We point out that models based on probability theory, and the statistical techniques derived from them, have limited applicability, at least in exploratory multivariate situations. Prior knowledge, if avail- able, must be incorporated into the analysis to yield greater stability. If prior knowledge is not available, however, it must not be invented. This applies to both the structural and the replication framework aspects of a model. The methods of gauging and stability analysis are introduced as alternatives. They use the notion of a technique as the pivot of data anal- ysis, not that of a model. Homogeneity analysis is used as an example.
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