Universal inference meets random projections: a scalable test for log-concavity

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-04-25 DOI:10.1080/10618600.2024.2347338

Robin Dunn, Aditya Gangrade, Larry Wasserman, Aaditya Ramdas

引用次数: 0

Abstract

Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric approaches to modeling distributions of data. The specific assumption of log-concavity is motivated ...

查看原文本刊更多论文

通用推理与随机投影：对数凹性的可扩展测试

形状约束在完全非参数和完全参数数据分布建模方法之间产生了灵活的中间地带。对数凹性这一特定假设的动机是...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.