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The performance of multivariate qual...

Wu, Chen-Hsiang.

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The performance of multivariate quality-control charts for autocorrelated bivariate data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The performance of multivariate quality-control charts for autocorrelated bivariate data./
Author:	Wu, Chen-Hsiang.
Description:	35 p.
Notes:	Source: Masters Abstracts International, Volume: 44-06, page: 2830.
Contained By:	Masters Abstracts International44-06.
Subject:	Applied Mechanics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1436361
ISBN:	9780542728471

The performance of multivariate quality-control charts for autocorrelated bivariate data.
Wu, Chen-Hsiang.

The performance of multivariate quality-control charts for autocorrelated bivariate data. - 35 p.

Source: Masters Abstracts International, Volume: 44-06, page: 2830.

Thesis (M.S.)--University of Maryland, College Park, 2006.

To monitor the mass production process, several quality control charts are constructed. Two of the most recognized schemes are the multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) schemes. Originally, we assume that the observations from the production process are independent. However, sometimes the observations are autocorrelated. In this article, a vector autoregressive model VAR (m) is applied. Here we want to study the impact of autocorrelations on both schemes. We also want to know about which scheme is more efficient when the observations are autocorrelated.

ISBN: 9780542728471Subjects--Topical Terms:

1018410
Applied Mechanics.

The performance of multivariate quality-control charts for autocorrelated bivariate data.
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040 $a UMI $c UMI
100 1 $a Wu, Chen-Hsiang. $3 1923265
245 1 4 $a The performance of multivariate quality-control charts for autocorrelated bivariate data.
300 $a 35 p.
500 $a Source: Masters Abstracts International, Volume: 44-06, page: 2830.
500 $a Adviser: Paul Zantek.
502 $a Thesis (M.S.)--University of Maryland, College Park, 2006.
520 $a To monitor the mass production process, several quality control charts are constructed. Two of the most recognized schemes are the multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) schemes. Originally, we assume that the observations from the production process are independent. However, sometimes the observations are autocorrelated. In this article, a vector autoregressive model VAR (m) is applied. Here we want to study the impact of autocorrelations on both schemes. We also want to know about which scheme is more efficient when the observations are autocorrelated.
590 $a School code: 0117.
650 4 $a Applied Mechanics. $3 1018410
690 $a 0346
710 2 0 $a University of Maryland, College Park. $3 657686
773 0 $t Masters Abstracts International $g 44-06.
790 1 0 $a Zantek, Paul, $e advisor
790 $a 0117
791 $a M.S.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1436361