Steven R. Howard, Aaditya Ramdas, Jon D. McAuliffe, J. Sekhon
{"title":"Time-uniform Chernoff bounds via nonnegative supermartingales","authors":"Steven R. Howard, Aaditya Ramdas, Jon D. McAuliffe, J. Sekhon","doi":"10.1214/18-ps321","DOIUrl":null,"url":null,"abstract":"We develop a class of exponential bounds for the probability that a martingale sequence crosses a time-dependent linear threshold. Our key insight is that it is both natural and fruitful to formulate exponential concentration inequalities in this way. We illustrate this point by presenting a single assumption and theorem that together unify and strengthen many tail bounds for martingales, including classical inequalities (1960-80) by Bernstein, Bennett, Hoeffding, and Freedman; contemporary inequalities (1980-2000) by Shorack and Wellner, Pinelis, Blackwell, van de Geer, and de la Pe\\~na; and several modern inequalities (post-2000) by Khan, Tropp, Bercu and Touati, Delyon, and others. In each of these cases, we give the strongest and most general statements to date, quantifying the time-uniform concentration of scalar, matrix, and Banach-space-valued martingales, under a variety of nonparametric assumptions in discrete and continuous time. In doing so, we bridge the gap between existing line-crossing inequalities, the sequential probability ratio test, the Cram\\'er-Chernoff method, self-normalized processes, and other parts of the literature.","PeriodicalId":46216,"journal":{"name":"Probability Surveys","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2018-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"115","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Probability Surveys","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1214/18-ps321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 115
Abstract
We develop a class of exponential bounds for the probability that a martingale sequence crosses a time-dependent linear threshold. Our key insight is that it is both natural and fruitful to formulate exponential concentration inequalities in this way. We illustrate this point by presenting a single assumption and theorem that together unify and strengthen many tail bounds for martingales, including classical inequalities (1960-80) by Bernstein, Bennett, Hoeffding, and Freedman; contemporary inequalities (1980-2000) by Shorack and Wellner, Pinelis, Blackwell, van de Geer, and de la Pe\~na; and several modern inequalities (post-2000) by Khan, Tropp, Bercu and Touati, Delyon, and others. In each of these cases, we give the strongest and most general statements to date, quantifying the time-uniform concentration of scalar, matrix, and Banach-space-valued martingales, under a variety of nonparametric assumptions in discrete and continuous time. In doing so, we bridge the gap between existing line-crossing inequalities, the sequential probability ratio test, the Cram\'er-Chernoff method, self-normalized processes, and other parts of the literature.
我们为鞅序列跨越时间相关线性阈值的概率建立了一类指数界。我们的主要见解是,以这种方式制定指数浓度不等式既自然又富有成效。我们通过提出一个单一的假设和定理来说明这一点,这些假设和定理统一并加强了鞅的许多尾界,包括Bernstein, Bennett, Hoeffding和Freedman的经典不等式(1960-80);当代不平等(1980-2000),作者:Shorack和Wellner、Pinelis、Blackwell、van de Geer和de la Pe ~na;以及Khan、Tropp、Bercu、Touati、Delyon等人的一些现代不平等现象(2000年后)。在每一种情况下,我们给出了迄今为止最强大和最一般的陈述,量化了离散和连续时间中各种非参数假设下标量、矩阵和巴拿赫空间值鞅的时间均匀浓度。在这样做的过程中,我们弥合了现有的跨线不等式、序列概率比检验、Cram - chernoff方法、自归一化过程和其他部分文献之间的差距。