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Lianjie Shu, Ph.D.

Affiliations: 
2002 Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 
Area:
Industrial Engineering
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"Lianjie Shu"
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Parents

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Fu-gee Tsung grad student HongKong University of Science and Technology
Fugee Tsung grad student 2002 HKUST
 (Cause -selecting charting techniques in multistage processes.)

Children

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Wenpo HUANG grad student 2013 University of Macau
Ruoyu Zhou grad student 2014 University of Macau
Jinyu Fan grad student 2018 University of Macau

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Publications

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Fan J, Shu L, Yang A, et al. (2020) Phase I analysis of high-dimensional covariance matrices based on sparse leading eigenvalues Journal of Quality Technology. 1-14
Li Y, Liu S, Shu L. (2019) Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data Renewable Energy. 134: 357-366
Yang A, Jiang X, Shu L, et al. (2018) Sparse bayesian kernel multinomial probit regression model for high-dimensional data classification Communications in Statistics - Theory and Methods. 48: 165-176
Huang W, Shu L, Jiang W. (2018) A gradient approach to efficient design and analysis of multivariate EWMA control charts Journal of Statistical Computation and Simulation. 88: 2707-2725
He F, Mao T, Hu T, et al. (2018) A new type of change-detection scheme based on the window-limited weighted likelihood ratios Expert Systems With Applications. 94: 149-163
Shu L, Fan J. (2018) A distribution-free control chart for monitoring high-dimensional processes based on interpoint distances Naval Research Logistics (Nrl). 65: 317-330
Fan J, Shu L, Zhao H, et al. (2017) Monitoring multivariate process variability via eigenvalues Computers & Industrial Engineering. 113: 269-281
Yang A, Xiang J, Shu L, et al. (2017) Sparse Bayesian Variable Selection with Correlation Prior for Forecasting Macroeconomic Variable using Highly Correlated Predictors Computational Economics. 51: 323-338
Huang W, Shu L, Woodall WH, et al. (2016) CUSUM procedures with probability control limits for monitoring processes with variable sample sizes Iie Transactions (Institute of Industrial Engineers). 1-13
Li Y, Shu L, Tsung F. (2016) A false discovery approach for scanning spatial disease clusters with arbitrary shapes Iie Transactions (Institute of Industrial Engineers). 48: 684-698
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