Comparison of control charts for autocorrelated data
Is this your thesis?
This record came from a bulk archive import. If it’s yours, link it to your profile.
Abstract (EN)
As a result of improvements in measurement techniques, sampling intervals become shorter, and this causes serial correlation in data. Also, in some process industries like chemical manufacturing and refinery operations serial correlation is inherent in consecutive measurements. To deal with this challenge, the traditional control charts are improved or new control charts are developed in the last few decades. Residual control charts such as X residual and EWMA residual are widely used control charts for autocorrelated data. In recent years, EWMAST, ARMAST, and DFTC charts have been also introduced for this type of data. To compare the performances of control charts have attracted interest of researchers. In the relevant literature, although there have been a lot of comparison studies, in only few of them the first-order autoregressive moving average (ARMA(1,1)) process have been investigated.The objective of this research is to compare performances of Shewhart X, CUSUM, X residual, EWMA residual, EWMAST, ARMAST, and DFTC charts for ARMA(1,1) process when the mean shifts. Performance criterion used for this comparison is the average run length (ARL).
Author
Şebnem Demirkol
How to Cite
Şebnem Demirkol (Master Thesis). Comparison of control charts for autocorrelated data, 2008, Dokuz Eylül University.
Keywords
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from Dokuz Eylül University
- The characteristic of rural architectural heritage and the conservation problem in Urla region(2019)
- AFAD gönüllülük sisteminin etkin müdahale açısından analiz(2020)
- Critics against Muawiyah ibn Abi Sufyan(2019)
- Synthesis, characterization and applications of polyphenol-Fe(III) complexes and tannic acid resin(2013)
- Türk resim sanatının son on yılına yönelik değerlendirmeler(1994)
- Rural life in Turkey (1960-1970)(2022)
