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Communications in Statistics - Theory and Methods Volume 42, 2013 - Issue 17
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Original Articles

The Generally Weighted Moving Average Variance Chart

Shey-Huei Sheu Department of Statistics and Informatics Science, Providence University, Taichung, Taiwan; Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, TaiwanCorrespondence[email protected]
&
ShinLi Lu Department of Industrial Management and Enterprise Information, Aletheia University, Taipei, Taiwan
Pages 3204-3214 | Received 05 Jun 2011, Accepted 05 Sep 2011, Published online: 12 Jul 2013

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Vasileios Alevizakos, Kashinath Chatterjee, Christos Koukouvinos & Angeliki Lappa. (2023) A double generally weighted moving average control chart for monitoring the process variability. Journal of Applied Statistics 50:10, pages 2079-2107.
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Vasileios Alevizakos, Kashinath Chatterjee, Christos Koukouvinos & Angeliki Lappa. (2021) A S 2-GWMA control chart for monitoring the process variability. Quality Engineering 33:3, pages 533-551.
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Ming Ha Lee & Michael B. C. Khoo. (2017) Synthetic double sampling s chart. Communications in Statistics - Theory and Methods 46:12, pages 5914-5931.
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Articles from other publishers (3)

Jen-Hsiang Chen & Shin-Li Lu. (2022) Economic-Statistical Performance of Auxiliary Information-Based Maximum EWMA Charts for Monitoring Manufacturing Processes. Mathematics 10:13, pages 2320.
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Kutele Mabude, Jean‐Claude Malela‐Majika, Philippe Castagliola & Sandile C. Shongwe. (2020) Generally weighted moving average monitoring schemes: Overview and perspectives. Quality and Reliability Engineering International 37:2, pages 409-432.
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Jen-Hsiang Chen & Shin-Li Lu. (2020) A New Sum of Squares Exponentially Weighted Moving Average Control Chart Using Auxiliary Information. Symmetry 12:11, pages 1888.
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