Foundations of Agnostic Statistics
Foundations of Agnostic Statistics
复制标题
不可知统计的基础
DOI：
10.1017/9781316831762
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
B. T. Miller
中科院分区：
文献类型：
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作者：
P. Aronow;B. T. Miller
反映了在过去三十年中如何进行实证研究的巨大变化，不可知统计学基础为社会和健康科学提供了现代统计理论的创新治疗。这本书发展了作者所谓的不可知统计学的基本原理，它考虑了在不假设存在一个简单的生成模型的情况下可以从世界上学到什么。Aronow和米勒为研究人员提供了统计推断的基础，这些研究人员不愿意做出超出他们或他们的听众认为可信的假设。从第一原则的建设，这本书涵盖的主题包括估计理论，回归，最大似然，缺失的数据和因果推理。使用这些原则，读者将能够正式阐明他们的调查目标，区分实质性假设和统计假设，并最终参与有助于人类知识的前沿定量实证研究。
Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true. Aronow and Miller provide the foundations for statistical inference for researchers unwilling to make assumptions beyond what they or their audience would find credible. Building from first principles, the book covers topics including estimation theory, regression, maximum likelihood, missing data, and causal inference. Using these principles, readers will be able to formally articulate their targets of inquiry, distinguish substantive assumptions from statistical assumptions, and ultimately engage in cutting-edge quantitative empirical research that contributes to human knowledge.