Bayesian approach to Phase II statistical process control for time series

Loading...

View/Download File

Zhou_umn_0130E_13603.pdf (507.57 KB)

Persistent link to this item

https://hdl.handle.net/11299/151570

Statistics

View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Bayesian approach to Phase II statistical process control for time series

Authors

Zhou, Tianyang

Published Date

2013-04

Publisher

Type

Thesis or Dissertation

Abstract

In statistical process control (SPC) problems, in-control values of parameters are required by traditional approaches. However this requirement is not realistic. New methods based on the change point model have been developed to avoid this requirement. The existing change-point methods are restricted to independent identically distributed observations, ignoring the numerous settings in which process readings are serially correlated. Furthermore, these frequentist methods are unable to make use of prior imperfect</DISS_para> <DISS_para>information on the parameters. In my research, I propose a Bayesian approach to the online SPC based on the change point model in an ARMA process. This approach accommodates serially correlated data, and also provides a coherent way of incorporating prior information on parameters.

Keywords

Bayesian

Monte Carlo

Phase II

Statistical Process Control

Time Series

Description

University of Minnesota Ph.D. dissertation. April 2013. Major: Statistics. Advisor: Douglas M. Hawkins. 1 computer file (PDF); vii, 78 pages.

Related to

Replaces

License

Collections

Dissertations

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Zhou, Tianyang. (2013). Bayesian approach to Phase II statistical process control for time series. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/151570.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.