Aggregated parameter update schemes for monitoring binary profiles

Abstract

Profile monitoring is one of the most important topics for statistical process control. Traditional self-starting profile monitoring schemes generally use all historical observations to estimate parameters. Because of the rapid increase in the complexity of modern statistical processes, the practitioners often need to deal with massive datasets in process monitoring. However, when observations of each period are of large sample size and the computation is of high complexity, the traditional method is not economical and urgently needs a parameter update strategy. Under the framework of binary profile monitoring, this paper proposes a novel recursive update strategy based on the aggregated estimation equation (AEE) for massive datasets and designs a self-starting control chart accordingly. Numerical simulation verifies that the proposed method performs better in parameter estimation and process monitoring. In addition, we give the asymptotic property of the proposed monitoring statistic and illustrate our method's superiority by a real-data example.

Keywords:

• Aggregated estimation equation
• binary profile monitoring
• massive dataset
• recursive update
• self-starting scheme

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Prof. Wu is supported by the National Natural Science Foundation of China (11871324); Prof. Hu is supported by the Scientific Research Project of Education Department of Zhejiang Province (Y202147034) and Zhejiang College of Shanghai University of Finance and Economics for Scientific Research Projects at the Provincial and Above Levels (2022).