算法1034:一种计算Qn鲁棒统计量的加速算法,并对常数进行校正

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING ACM Transactions on Mathematical Software Pub Date : 2022-12-16 DOI:10.1145/3576920
Thierry Fahmy
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引用次数: 0

摘要

由Croux和Rousseeuw开发的鲁棒尺度估计器Qn,为其计算提供了一种确定性算法,已被证明在质量管理和时间序列分析等多个领域非常有用。它具有有趣的数学(50%击穿,82%渐近相对效率)和计算(O(nlogn)时间,O(n)空间)特性。在研究更快的算法来计算Qn时,我们发现了d常数计算中的一个错误,结果是用于缩放统计量以与正常样本的方差一致的dn常数。这些错误已经在包括国际标准组织13528[12]文件在内的几篇文章中重复出现。在本文中,我们修复了这些错误,并提出了一种新的方法,其中包括一个新的算法,当n从10增加到100,000时,计算速度可以提高1.3到4.5倍。
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Algorithm 1034: An Accelerated Algorithm to Compute the Qn Robust Statistic, with Corrections to Constants
The robust scale estimator Qn developed by Croux and Rousseeuw [3], for the computation of which they provided a deterministic algorithm, has proven to be very useful in several domains including in quality management and time series analysis. It has interesting mathematical (50% breakdown, 82% Asymptotic Relative Efficiency) and computing (O(nlogn) time, O(n) space) properties. While working on a faster algorithm to compute Qn, we have discovered an error in the computation of the d constant, and as a consequence in the dn constants that are used to scale the statistic for consistency with the variance of a normal sample. These errors have been reproduced in several articles including in the International Standard Organisation 13,528 [12] document. In this article, we fix the errors and present a new approach, which includes a new algorithm, allowing computations to run 1.3 to 4.5 times faster when n grows from 10 to 100,000.
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来源期刊
ACM Transactions on Mathematical Software
ACM Transactions on Mathematical Software 工程技术-计算机:软件工程
CiteScore
5.00
自引率
3.70%
发文量
50
审稿时长
>12 weeks
期刊介绍: As a scientific journal, ACM Transactions on Mathematical Software (TOMS) documents the theoretical underpinnings of numeric, symbolic, algebraic, and geometric computing applications. It focuses on analysis and construction of algorithms and programs, and the interaction of programs and architecture. Algorithms documented in TOMS are available as the Collected Algorithms of the ACM at calgo.acm.org.
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