大数据的统计方法和计算
Statistical Methods and Computing for Big Data
摘要：
大数据是指在数量、强度和复杂性方面超过标准软件工具能力的大规模数据。它们给统计人员带来了机遇，也带来了挑战。计算统计学家在大数据分析中的科学发现中的作用甚至没有得到同行统计学家的认可。本文回顾了统计学中最近的方法和软件开发，以应对大数据挑战。方法分为三类：基于子采样，分而治之，和顺序更新流数据。软件评论侧重于开源R和R包，涵盖有助于打破计算机内存和计算能力障碍的最新工具。一些工具说明了一个案例研究与逻辑斯谛回归的机会，航空公司延误。
英文摘要：
Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard software tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article reviews recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and sequential updating for stream data. Software review focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
