Applications of recursive estimation methods in statistical process control

Authors

Shelton Peiris
Aerambamoorthy Thavaneswaran
David E. AllenFollow
R. Mellor

Document Type

Other

Publisher

Edith Cowan University

Place of Publication

Perth, Western Australia

School

School of Accounting, Finance and Economics

Comments

Peiris, S., Thavaneswaran, A., Allen, D., & Mellor, R. (2003). Applications of Recursive Estimation Methods in Statistical Process Control. Perth, Australia: Edith Cowan University.

Abstract

In recent years there has been a growing interest in recursive estimation techniques as applied to statistical process control (SPC). In cases where prior information about the processes are available, it is shown that procedures based on the “optimal” smoothing can be superior to the classical procedures like Shewhart’s CUSUM control charts (see, for instance, Thavaneswaran, McPherson and Abraham (1998)). This paper reviews the recursive algorithms based on EWMA (exponentially weighted moving average), DLM (dynamic linear modeling), KF (Kalman filtering) and OS (optimal smoothing) in statistical process control with correlated data. We also discuss various relationships among the asymptotic mean square errors (MSE) of these procedures in SPC.