In Praise of Simplicity not Mathematistry! Ten Simple Powerful Ideas for the Statistical Scientist

• 成果类型: Article
• 署名作者: Little, Roderick J.
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.787932
• 发表日期: 2013
• 页码: 359-369
• 关键词: calibrated bayes; penalized spline; inference; regression; MODEL; likelihood; imputation; outcomes; SUBJECT; DESIGN

摘要

Ronald Fisher was by all accounts a first-rate mathematician, but he saw himself as a scientist, not a mathematician, and he railed against what George Box called (in his Fisher lecture) mathematistry. Mathematics is the indispensable foundation of statistics, but for me the real excitement and value of our subject lies in its application to other disciplines. We should not view statistics as another branch of mathematics and favor mathematical complexity over clarifying, formulating, and solving real-world problems. Valuing simplicity, I describe 10 simple and powerful ideas that have influenced my thinking about statistics, in my areas of research interest: missing data, causal inference, survey sampling, and statistical modeling in general. The overarching theme is that statistics is a missing data problem and the goal is to predict unknowns with appropriate measures of uncertainty.