Abstract Distinguishing two candidate models is a fundamental and practically important statistical problem. Error rate control is crucial to the testing logic but, in complex nonparametric settings, can be difficult to achieve, especially when the stopping rule that determines the data collection process is not available. This paper proposes an e-process construction based on the predictive recursion algorithm originally designed to recursively fit nonparametric mixture models. The resulting predictive recursion process affords anytime valid inference and is asymptotically efficient in the sense that its growth rate is first-order optimal relative to the predictive recursion’s mixture model.

Vaidehi Dixit

Ryan Martin

Biometrika

BIOMETRIKA 主要是一本统计学杂志，其中重点放在包含在应用中具有直接或潜在价值的原始理论贡献的论文上。不时还会发表一些边缘领域的论文。

Biometrika is a journal primarily focused on statistics, particularly papers that present original theoretical contributions with direct or potential value in applications. Occasionally, it also publishes research from peripheral areas.

《Biometrika》是一本主要关注统计学的期刊，特别是那些在应用中具有直接或潜在价值的原创理论贡献的论文。此外，期刊偶尔也会发表一些边缘领域的研究成果。

Category	Quartile
数学	2
数学, 数学与计算生物学	1
数学, 生物学	2
数学, 统计学与概率论	2

Anytime valid and asymptotically efficient inference driven by predictive recursion

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
STATISTICS & PROBABILITY	1