Charts for Detecting Small Shifts

Sivasamy R. and Jayanthi S.
Additional contact information
Sivasamy R.: Statistics Department, Annamalai University, Annamalai Nagar 608 002, India
Jayanthi S.: Statistics Department, Annamalai University, Annamalai Nagar 608 002, India

Stochastics and Quality Control, 2003, vol. 18, issue 1, 91-100

Abstract: Some recent studies have proved that charts with variable sample size (VSS) detect moderate or larger shifts in normal and stable processes more quickly than the Standard Shewhart (SS) chart. But the statistical efficiency of charts, the speed at which they detect smaller shifts of the process mean are poor. In this paper a special type of Markov dependent sample size (MDSS) chart is discussed that can detect even small shifts faster than other matched charts. Additionally, conditions are outlined which make the MDSS chart signal similar as the SS or VSS chart during the in-control period. Numerical illustrations indicate that the MDSS chart is substantially more efficient than SS and VSS charts.

Keywords: Markov Dependent Sampling Scheme; Markov Dependent Sample Size; Variable Sample Size; Stationary Probabilities; Average run length (search for similar items in EconPapers)
Date: 2003
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/EQC.2003.91 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ecqcon:v:18:y:2003:i:1:p:91-100:n:9

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/eqc/html

DOI: 10.1515/EQC.2003.91

Access Statistics for this article

Stochastics and Quality Control is currently edited by George P. Yanev

More articles in Stochastics and Quality Control from De Gruyter
Bibliographic data for series maintained by Peter Golla ().