Monitoring individuals with irregular semiparametric longitudinal behaviour

Abstract

In our daily life, identifying individuals whose longitudinal behaviour differs from the behaviour of those well-functioning individuals is often necessary to avoid some unpleasant consequences. For such purposes, this paper proposes a new charting scheme called semiparametric screening system in cases when the longitudinal behaviour is semiparametric, using SPC and longitudinal data analysis techniques. Various cases, including those with equally spaced data points, unequally spaced data points, temporal correlated data, non-normal data, are discussed by simulations. Also our proposed method is demonstrated using two real data examples about the SHARe Framingham Heart Study of the National Heart, Lung and Blood Institute, and the moisture content of cut tobacco at the outlet collected in Shanghai tobacco group co., LTD of China, respectively. All the numerical results show that the proposed method works well in practice.

Keywords:

• Semiparametric longitudinal data analysis
• statistical process control
• decorrelation

Acknowledgements

The authors thank the Guest editor Prof. Amitava Mukherjee and three referees for many constructive comments and suggestions, which have greatly improved the quality of the article.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by National Science Fund of China [grant number 11501209], [grant number 11271135], [grant number 11571113], [grant number 71402133] and [grant number 71602155], the Postdoctoral Science Foundation of China [grant number 2015M570348], Shanghai Rising Star Program [grant number 16QA1401700], the Fundamental Research Funds for the Central Universities and the 111 Project [grant number B14019], The International Postdoctoral Exchange Fellowship Program [grant number 20160089], Program of Shanghai Subject Chief Scientist [grant number 14XD1401600] and the Project of Shanghai Universities to enhance the competition and innovation collaborative innovation of modern statistical methods and theory.