Exploratory data analysis for complex models
Exploratory data analysis for complex models
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DOI：
10.1198/106186004x11435
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发表时间：
2004-12-01
影响因子：
2.4
通讯作者：
Gelman, A
中科院分区：
文献类型：
--
作者：
Gelman, A
“探索性”和“验证性”数据分析都可以被看作是比较观察到的数据与根据隐式或显式统计模型获得的数据的方法。例如，Tukey的许多方法可以被解释为对假设线性模型和泊松分布的检查。在更复杂的情况下。贝叶斯方法可用于为探索性数据分析中有用的各种图构建参考分布。本文提出了一种将探索性数据分析与基于概率模型的更正式的统计方法统一起来的方法。这些想法是在包括心理学在内的前沿领域的例子的背景下发展起来的。药和社会科学。
"Exploratory" and "confirmatory" data analysis can both be viewed as methods for comparing observed data to what Would be obtained tinder an implicit or explicit statistical model. For example, many of Tukey's methods can be interpreted as checks against hypothetical linear models and Poisson distributions. In more complex situations. Bayesian methods can be useful for constructing reference distributions for various plots that are useful in exploratory data analysis. This article proposes an approach to unify exploratory data analysis with more formal statistical methods based on probability models. These ideas are developed in the context of examples front fields including psychology. medicine. and social science.