Liu Liu

Prof. Liu Liu

orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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29 July 2013 in International Journal of Production Research
Verified
Journal Article
Closed access
Liu LiuLiu Liu
Liu Liu
FTFugee Tsung
Fugee Tsung
JZJian Zhang
Jian Zhang
In this paper, we provide a sequential rank-based adaptive nonparametric cumulative sum control chart for detecting a range of shifts in the location parameter. This chart is a self-starting scheme, and thus can be used to monitor processes at the start-up stages rather than having to wait for the accumulation of sufficiently large calibration samples. It does not require any prior knowledge of the underlying distribution. The choice of the chart parameters is studied and a simulation study demonstrates that the proposed control chart not only performs robustly for different distributions, but also efficiently detects various magnitudes of shifts. An illustrative example is also given to introduce the implementation of this proposed control chart.

1 April 2013 in Communications in Statistics - Simulation and Computation
Verified
Journal Article
Closed access
Liu LiuLiu Liu
Liu Liu
XZXuemin Zi
Xuemin Zi
JZJian Zhang
Jian Zhang
ZWZhaojun Wang
Zhaojun Wang
Nonparametric control chart is useful when the underlying distribution is unknown, or is not likely to be normal. In this article, we provide a sequential rank-based nonparametric adaptive EWMA (NAE) control chart for detecting the persistent shift in the location parameter. This NAE chart is a self-starting scheme and thus can be used to monitor processes at the start-up stages rather than waiting for the accumulation of sufficiently large calibration samples. Moreover, we do not require any prior knowledge of the underlying distribution, and to prespecify any tuning parameter either. A Markov chain model is suggested to calibrate the run-length distribution of NAE, which is shown to have approximate tail probability as a geometric distribution. A simulation study demonstrates that the proposed control chart not only performs robustly for different distributions, but also is efficient in detecting various magnitude of shifts. A real-data example from manufacturing shows that it performs quite well in practical applications.

16 October 2014 in Quality and Reliability Engineering International
Verified
Journal Article
Closed access
PDF file
Liu LiuLiu Liu
Liu Liu
BCBin Chen
Bin Chen
JZJian Zhang
Jian Zhang
XZXuemin Zi
Xuemin Zi

1 December 2017 in Applied Mathematical Modelling
Verified
Journal Article
Closed access
JYJin Yue
Jin Yue
Liu LiuLiu Liu
Liu Liu

7 June 2017 in Statistics in Medicine
Verified
Journal Article
Closed access
PDF file
JYJin Yue
Jin Yue
XLXin Lai
Xin Lai
Liu LiuLiu Liu
Liu Liu
PLPaul B. S. Lai
Paul B. S. Lai

1 February 2021 in Computers & Industrial Engineering
Verified
Journal Article
Closed access
QHQiuyan Hu
Qiuyan Hu
Liu LiuLiu Liu
Liu Liu