Good randomized sequential probability forecasting is always possible

• 成果类型: Article
• 署名作者: Vovk, V; Shafer, G
• 署名单位: Rutgers University System; Rutgers University New Brunswick; Rutgers University Newark; University of London; Royal Holloway University London
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2005.00525.x
• 发表日期: 2005
• 页码: 747-763
• 关键词: Calibration; FOUNDATIONS

摘要

Building on the game theoretic framework for probability, we show that it is possible, using randomization, to make sequential probability forecasts that will pass any given battery of statistical tests. This result, an easy consequence of von Neumann's minimax theorem, simplifies and generalizes work by earlier researchers.