A note on detecting outliers in short autocorrelated data using joint estimation and exponentially weighted moving average

被引：3
|
作者：
Wright, CM [1 ]
Hu, MY
机构：
[1] Cent Missouri State Univ, Harmon Coll Business, Dept Management, Warrensburg, MO 64093 USA
[2] Kent State Univ, Coll Business Adm, Dept Marketing, Kent, OH 44242 USA
来源：
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE | 2003年 / 31卷 / 04期
关键词：
quality; SPC; time series; non-iid data; autocorrelated data; short-run data;
D O I：
10.1016/S0305-0483(03)00046-X
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
Comprehensive results for the joint estimation outlier detection method and the exponentially weighted moving average method with regard to their performance as statistical process control methods or outlier detection methods for short-run autocorrelated data are reported. Both methods are shown to be effective. Extensive tables are presented which may be used by practitioners to determine the best time-series lengths and smoothing constants or critical values for use with these methods. In addition, several of the tables report results at the lowest level for five factors. (C) 2003 Elsevier Science Ltd. All rights reserved.
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页码：319 / 326
页数：8
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