The research objective of this project lies in the interface between statistics  and management science or industrial engineering, particularly on statistical  quality control (SQC).

Most of the traditional research on SQC focused on the quality of a process  that can be adequately represented by the univariate or multivariate  distribution of several specific quality characteristics. However, in many  practical situations, say electronic products processes, the quality of a  process is better characterized by a relationship between a response variable  and one or more explanatory variables, i.e., profile monitoring, which offers  great challenges to the conventional on-line monitoring methods. The problem of  profile monitoring has been one of the most hot and difficult problems since its  introduction in 2000. Most of the research in the literature focused on  monitoring simple linear profiles before 2008. Two of our papers, published in  the top journal of international industrial statistics Technometrics by the  American Society for Quality and the American Statistical Association in 2008  and 2010 respectively, successfully solved the problem of monitoring complex  profiles with arbitrary curves. The Editor commented our paper that this is a  valuable piece of work that provides a complete and solid solution for  statistical monitoring of profiles. We published more than 10 papers along this  research direction. Among them, one is the essential science indicators (ESI)  highly cited papers, one is the 2nd of the most 10 highly cited papers during  2006-2008 in the flagship journal IIE Transactions by the International  Institute of Industrial Engineers, two are the 3rd and 5th of the most 5 highly  cite papers in Technometrics, and one is published in Technometrics as  discussion paper, which is one of the 7 discussion papers in this journal during  2000-2010, and the researchers who discussed this paper are all world famous in  the area of industrial statistics. Moreover, this paper was invited to be  presented at the joint statistical meetings (JSM) in 2010, which is the largest  scale statistical meeting in the world. It is a tradition that international top  statistical journals publish discussion papers, but the proportion of discussion  papers will not be more than 3% of the totally published papers. Furthermore,  some of our research are cited in paragraphs more than 60 times in the book  Statistical Analysis of Profile Monitoring published by the press Wiley. As  for multivariate data streams and multi-stage process control, we made full use  of the dimension reduction and variable selection methods in modern statistics  to provide a unified framework for SQC and diagnosis. Many SQC research teams  noticed and focused on our research, and made further research following ours.  In the chapter nonparametric (distribution-free) quality control charts of the  Encyclopedia of Statistical Sciences, the author Prof. Subha Chakraborti cited  our research on nonparametric SQC in paragraphs.

In summary, we published 49 science citation indexed (SCI) papers. Among  them, two are in the journal Journal of American Statistical Association, nine  are in the journal Technometrics, six are in the journal Journal of Quality  Technology, three are in the journal IIE Transactions. All these papers are  cited 351 times and the most one is even cited by 61 times. As members of the  research team for this project, two of us won The National Excellent Doctoral  Dissertation Award and Award nomination respectively, one of us won the Jiaqing  Zhong Scholarship by the Chinese Mathematical Society, one of us won the Youth  Science and Technology Award in Tianjin, one of us won the Outstanding Young  Scholars of the First 10 Thousand Scholars Program by the Organization  Department of the Central Committee of China.


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