The non-asymptotic tail bounds of random variables play crucial roles in probability, statistics, and machine learning. Despite much success in developing upper tail bounds in literature, the lower tail bound results are relatively fewer. In this partly expository paper, we introduce systematic and user-friendly schemes for developing non-asymptotic lower tail bounds with elementary proofs. In addition, we develop sharp lower tail bounds for the sum of independent sub-Gaussian and sub-exponential random variables, which matches the classic Hoeffding-type and Bernstein-type concentration inequalities, respectively. We also provide non-asymptotic matching upper and lower tail bounds for a suite of distributions, including gamma, beta, (regular, weighted, and noncentral) chi-squared, binomial, Poisson, Irwin-Hall, etc. We apply the result to establish the matching upper and lower bounds for extreme value expectation of the sum of independent sub-Gaussian and sub-exponential random variables. A statistical application of signal identification from sparse heterogeneous mixtures is finally studied.
翻译:随机变量的非隐性尾线在概率、统计和机器学习方面起着关键作用。尽管在文献中开发上尾线取得了很大成功,但下尾线效果相对较少。在本部分解释性文件中,我们采用系统和方便用户的办法,利用基本证据开发非隐性下尾线;此外,我们开发了与传统的Hoffding型和Bernstein型浓度不平等相匹配的独立亚高低热量随机变量之和的尖锐低尾线。我们还为一组分布型(包括伽马、β、(常规、加权和非中性)、彩色、binomial、Poisson、Irwin-Hall等)提供了非隐性匹配的上下尾线。我们应用这一结果,为独立亚丁型和亚显性随机变量之和极值的极端值设定匹配的上下边线。我们最终研究了从稀异混合混合物中识别信号的统计应用。