个人简介

Academic QualificationsPhD in Industrial Engineering & Engineering Management, The Hong Kong University of Science & Technology, Hong Kong, 2002B.Sc in Mechanical Engieering & Automation, Xi-an Jiao Tong University, China, 1998Related Working ExperiencesProfessor, Faculty of Business Administration, University of Macau, 2014~PresentAssociate Professor, Faculty of Business Administration, University of Macau, 2008~2014Assistant Professor, Faculty of Business Administration, University of Macau, 2002~2008Visiting Associate Professor, Department of MEEM, City University of Hong Kong, Sept. 2010-Jan. 2011Visiting Scholar, Department of Industrial Engineering & Engineering Management, HKUST, 2005.Visiting Scholar, Department of Industrial Engineering, Texas A&M University, 2002.TeachingUndergraduate CoursesQuantitative Decision Analysis (ISOM3030)Statistical Quality Control (QMDS310)Quantitative Decision Analysis (QMDS300)Business Mathematics (MSOR100)Survey Calculus (MSOR103)Statistic I (MSOR210)Statistic II (MSOR211)Postgraduate CoursesBusiness Decision Tools (IMB109)China Business Seminar & Field Study (IMB100)Business Decision Tools (IMBC102)

研究领域

High-dimensional StatisticsFinancial EngieeringStatistical Quality ControlStatistical Learning

Research ProjectsA Distribution Free Control Chart for Monitoring High-Dimensional Processes base on Interpoint Distances, PI, MYRG2018-00087-FBA, Mop $834,000, 2019-2022.Improving minimum-variance portfolios based on Schatten norms, FDCT/0064/2018/A2, Mop $789,000, PI, 2019-2022.Efficient design and analysis of statistical control charts with dynamic control limits, FDCT/053/2015/A2, Mop $1,123,000, PI, 2016-2019.Statistical Monitoring and Diagnosis of High-Dimensional Processes, MYRG2016-00012-FBA, Mop $815,000, PI, 2017-2020.A Gradient Approach for Efficient Design and Sensitivity Analysis of Control Charts Under Shift Uncertainty, FDCT/002/2013/A, Mop $488,000, PI, 2013-2015.Accurate Evaluation of Control Charts under Skewed Distributions, PI, MYRG090(Y1-L2)-FBA13-SLJ, Mop $540,000, 2013-2016.Monitoring Count Data with Varying Sample Sizes, PI, MYRG096(Y1-L2)-FBA12-SLJ, Mop $560,000, 2012-2015.Surveillance Strategies for Detecting Increases in Incidence Rate based on Weighted CUSUM Procedures, PI, MYRG164(Y1-L3)-FBA11-SLJ, Mop $840,000, 2011-2013.A Robust CUSUM Approach to Monitoring Process Variance Changes, funded by the Research Committee of University of Macau, 2009-2010, Principal InvestigatorA New Resetting Scheme for the Exponentially Weighted Moving Average Control Chart, funded by the Research Committee of University of Macau, 2007-2009, Principal InvestigatorStatistical Process Control of Process Dispersion When Parameters Are Unknown, funded by the Research Committee of University of Macau, 2006-2007, Principal InvestigatorAdaptive Exponentially Weighted Moving Average Control Charts for Monitoring Process Variances, funded by the Research Committee of University of Macau, 2005-2006, Principal InvestigatorMonitoring Complex Data in Dynamic Systems, funded by the Research Committee of University of Macau, 2004-2005, Principal InvestigatorOn the Monitoring of Process Mean over a Range of Shift Sizes, funded by the Research Committee of University of Macau, 2003-2004, Principal InvestigatorDynamic Process Monitoring, funded by the Research Committee of University of Macau, 2002-2003, Principal Investigator多元复杂时空数据建模与监控方法研究, NSFC #71672109, RMB480,000, Co-Investigator, 2017-2020.质量管理中高维数据的统计过程控制研究, NSFC #71172131, RMB420,000, Co-investigator, 2012-2015.长记忆过程转变点的监测及有自相关扰动项的过程调整研究, NSFC #71102145, RMB 220,000 Co-Investigator, 2012-2014.

近期论文

Shu, L., Apley, D. and Tsung, F. (2002). Triggered Cuscore Charts for Monitoring Autocorrelated Processes, Quality & Reliability Engineering International, 18, 411-421.Shu, L. and Tsung, F. (2003). On Multistage Statistical Process Control, Journal of Chinese Institute of Industrial Engineers, 20, 1-8.Shu, L., Tsung, F. and Kapur, K. (2004). Monitoring and Diagnosis with Multiple Cause-Selecting Control Charts, Quality Engineering, 16, 437-450.Shu, L., Tsung, F., and Tsui, K.-L. (2004). Run-Length Performance of Regression Control Charts with Estimated Parameters, Journal of Quality Technology, 36, 280-292, 2004.Shu, L., Tsung, F. and Tsui, K.-L. (2005). Effects of Estimation Errors on the Performance of Cause-Selecting Charts, IIE Transactions, 37, 559-567 (ABS3).Shu, L. and Jiang, W. (2006). A Markov Chain Model for the Adaptive CUSUM Control Chart, Journal of Quality Technology, 38, 135-147.Jiang, W., Shu, L. and Tsung, F. (2006). A Comparison of Joint Monitoring Schemes for APC-Controlled Processes, Quality & Reliability Engineering International, 22, 939-952.Shu, L., Jiang, W., and Wu, S. (2007). A One-Sided EWMA Control Chart for Monitoring Process Means, Communication in Statistics: Simulation and Computation, 36, 901-920.Shu, L. (2008). An Adaptive Exponentially Weighted Moving Average Control Chart for Monitoring Process Variances, Journal of Statistical Computation and Simulation, 78(4), 367-384.Shu, L., Jiang, W. and Tsui, K.-L. (2008). A Weighted CUSUM Chart for Detecting Patterned Mean Shifts, Journal of Quality Technology , 40, 194-213.Jiang, W., Shu, L. and Apley, D. (2008). Adaptive CUSUM Procedures with EWMA-based Shift Estimators, IIE Transactions, 40(10), 992-1103 (ABS3).He, F., Jiang, W., and Shu, L. (2008). Improved Self-Starting Control Charts for Short Runs, Quality Technology and Quantitative Management, 5(3), 289-308.Shu, L. and Jiang, W. (2008). A New EWMA Chart for Monitoring Process Dispersion, Journal of Quality Technology , 40, 319-331.Shu, L., Jiang, W. and Wu, Z. (2008). Adaptive CUSUM Procedures with Markovian Mean Estimation, Computational Statistics and Data Analysis , 52(9), 4395-4409 (ABS3).Wu Z., Khoo M. B. C., Shu, L. and Jiang W. (2009). An np Control Chart for Monitoring the Mean of a Variable Based on An Attribute Inspection, International Journal of Production Economics, 121(1), 141-147 (ABS3).“Liu Y. F., He Z., Shu, L. and Wu Z. (2009). Statistical Computation andAnalyses for Attribute Events, Computational Statistics and Data Analysis , 53, 3412-3425 (ABS3). “Shu, L., Jiang, W., and Yeung, H. F. (2010). An Adaptive CUSUM Procedure for Signaling Process Variance Changes of Unknown Sizes, Journal of Quality Technology, 42, 69-85.Shu, L., Jiang, W., and Tsui, K.-L. (2011). Comparison of Weighted CUSUM Procedures that Account for Monotone Changes in Population Size, Statistics in Medicine, 30(7), 725-741. (Honarable Mention Award in IEEE on IEEM 2010 conference)Su, Y., Shu, L., and Tsui, K.-L. (2011). Adaptive EWMA Procedures for Monitoring Processes Subject to Linear Drifts, Computational Statistics and Data Analysis, 55, 2819-2829 (ABS3).Jiang, W., Shu, L., and Tsui, K.-L. (2011). Weighed CUSUM Control Charts for Monitoring Inhomogeneous Poisson Processes with Varying Sample Sizes, Journal of Quality Technology, 43, 346-362.Su, Y., Chan, L., Shu, L., and Tsui, K.-L. (2012). Real Time Prediction Models for Output Power and Efficiency of Grid-Connected Solar Photovoltaic Systems, Applied Energy, 93, 319-326.Shu, L., Jiang, W., and Wu, Z. (2012). Exponentially Weighted Moving Average Control Charts for Monitoring Increases in Poisson Rate, IIE Transactions, 44(9), 711-723 (ABS3).“Huang, W., Shu, L., and Jiang, W. (2012). Evaluation of Exponentially Weighted Moving Variance Control Chart Subject to Linear Drifts, Computational Statistics and Data Analysis, 56,4278-4289 (ABS3).”Shu, L., Jiang, W., and Tsui, K.-L. (2012). A Standardized Scan Statistic for Detecting Spatial Clusters with Estimated Parameters, Naval Research Logistics, 59, 397-410 (ABS3).Jiang, W., Shu, L., Zhao, H., and Tsui, K.-L. (2013). CUSUM Procedures for Health Care Surveillance, Quality & Reliability Engineering International, 29(6), 883-897.“Shu, L., Huang, W., Su, Y., and Tsui, K.-L. (2013). Computation of Run Length Percentiles of CUSUM Control Charts under Changes in Variances, Journal of Statistical Computationand Simulation, 83, 1238-1251.”“Huang, W., Shu, L., Jiang, W. and Tsui, K.-L. (2013). Evaluation of Run-Length Distribution for CUSUMCharts under Gamma Distributions, IIE Transactions, 45, 981-994 (ABS3).”Qu, L., Wu, Z., Khoo M. B. C., Shu, L. (2014). A New Control Chart for Monitoring the Event Frequency and Magnitude, European Journal of Industrial Engineering, 8, 789-813 (ABS2).He, F., Shu, L, and Tsui, K.-L. (2014). Adaptive CUSUM Charts for Monitoring Linear Drifts in Poisson Rates, International Journal of Production Economics, 148, 14-20 (ABS3).Huang, W., Shu, L., and Su, Y. (2014). An Accurate Evaluation of Adaptive Exponentially Weighted Moving Average Schemes, IIE Transactions, 46, 457-469 (ABS3).Shu, L., Su, Y., Jiang, W., and Tsui, K.-L. (2014). A Comparison of Exponentially Weighted Moving Average Based Methods for Monitoring Increases in Incidence Rate with Varying Population Size, IIE Transactions, 46(8), 798-812 (ABS3).Zhou, Q., Huang, W., and Shu, L. (2014). A Comparison of Weighted CUSUM Procedures for Monitoring Process Proportions with Varying Sample Sizes”, International Journal of Production Research, 3225-3238 (ABS3).Li, Y., Su, Y., and Shu, L. (2014). An ARMAX Model for Forecasting the Power Output of a Grid Connected Photovoltaic System, Renewable Energy, 66, 78-89.Shu, L., Huang, W., and Jiang, W. (2014). A Novel Gradient Approach for Optimal Design and Sensitivity Analysis of EWMA Control, Naval Research Logistics, 61, 223-237 (ABS3).Shu, L., Ling, M.H., Wong. S.Y., Tsui, K.L. (2014). Spatial Clustering in Public Health: Advances and Challenges. In: Yang, H., Kundakcioglu, E.. Healthcare Intelligence: Turning Data into Knowledge, IEEE Intelligent Systems, 29(3):65-68.Han, S.W., Jiang W., Shu L., and Tsui, K.-L. (2015). A Comparison of Likelihood-based Spatiotemporal Surveillance Methods, Communications in Statistics: Simulation and Computation, 44(1), 14-39.Zhou, R., Shu, L., and Su, Y. (2015). An Adaptive Minimum Spanning Tree Test for Detecting Irregularly-Shaped Spatial Clusters, Computational Statistics and Data Analysis, 89, 134-146 (ABS3).Zhao, H., Shu, L., and Tsui, K.-L. (2015). “A Window-Limited Generalized Likelihood Ratio Test for Monitoring Poisson Processes with Linear Drifts, Journal of Statistical Computation and Simulation, 85(15), 2975–2988.Shu, L., Zhou, R., and Su, Y. (2016). A Self-Adjusted Weighted Likelihood Ratio Test for Global Clustering of Disease, Journal of Statistical Computation and Simulation, 86(5), 996-1009.Zhou, Q., Shu, L., and Jiang, W. (2016). One-sided EWMA Control Charts for Monitoring Processes with Varying Sample Sizes”, Communications in Statistics: Theory and Methods, 45, 6112-6132 .Li, J., Yu, N., Liu, Z., and Shu, L. (2016). Optimal Rebate Strategies in a Two-echelon Supply Chain with Nonlinear and Linear Multiplicative Demands, Journal of Industrial and Management Optimization, 12(4), 1587-1611 (ABS1).Huang, W., Shu, L., Jiang W. (2016). A Gradient Approach to the Optimal Design of CUSUM Charts Under Unknown Mean Shift Sizes, Journal of Quality Technology, 48, 68-83.Wang, G., Su, Y., and Shu, L. (2016). One-day-ahead Daily Power Forecasting of Photovoltaic Systems based on Partial Functional Linear Regression Models, Renewable Energy, 96, 469-478.Li, Y., He, Y., Su, Y., and Shu, L. (2016). Forecasting the Daily Power Output of a Grid-connected Photovoltaic System based on Multivariate Adaptive Regression Splines, Applied Energy, 180, 392-401.Huang, W., Shu, L., Woodall, W.H., and Tsui, K.-L. (2016). CUSUM Procedures with Probability Control Limits for Monitoring Processes with Variable Sample Sizes, IIE Transactions, 48(8), 759-771 (ABS3).Li, Y., Shu, L., and Tsung, F. (2016).A False Discovery Approach for Scanning Spatial Disease Clusters with Arbitrary Shapes, IIE Transactions, 48(7), 684-698. (ABS3)Huang, W., Shu, L., Cao, J., and Tsui, K.-L. (2016). Probability Distribution of CUSUM Charting Statistics, IIE Transactions, 48(4), 324-332 (ABS3).Yang, A., Jiang, X., Shu, L., and Lin, J. (2017). Bayesian Variable Selection with Sparse and Correlation Priors for High-dimensional Data Analysis, Computational Statistics, 32, 127-143 (ABS2).Fan, J, Shu, L., Zhao, H., and Yeung, H. (2017). Monitoring Multivariate Process Variability via Eigenvalues, Computer & Industrial Engineering, 113, 269-281 (ABS2).He, F., Mao, T., Hu, T., and Shu, L. (2018). A New Type of Change-Detection Scheme based on the Window-Limited Weighted Likelihood Ratios, Expert Systems with Applications, 94, 149-163 (ABS3).Su, Y., Zhang, Y., and Shu, L. (2018). Experimental Study of Using Phase Change Material Cooling in a Solar Tracking Concentrated Photovoltaic-Thermal System, Solar Energy, 159, 777-785.Yang, A., Xiang, J., Shu, L., and Yang, H. (2018). Sparse Bayesian Variable Selection with Correlation Prior for Forecasting Macroeconomic Variable using Highly Correlated Predictors, Computational Economics,51, 323-338.Huang, W., Shu, L., and Jiang, W. (2018). A Gradient Approach to Efficient Design and Analysis of Multivariate EWMA Control Charts, Journal of Statistical Computation and Simulation, 88, 2705-2725.Shu, L., and Fan, J. (2018). A Distribution‐free Control Chart for Monitoring High‐dimensional Processes based on Interpoint Distances, Naval Research Logistics,65, 317-330 (ABS3).Li, Y., Liu, S., and 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., and Liu, P. (2019). Sparse Bayesian Kernel Multinomial Probit Regression Model for High-dimensional Data Classification, Communications in Statistics: Theory and Methods, 48, 165-176.Shi, F., Shu, L., Yang, A., and He, F. (2019). Improving Portfolio Performance by Alleviating Over-dispersion of Eigenvalues, Journal of Financial and Quantitative Analysis, accepted, (FT50, ABS4).Shu, L., Shi, F., and Tian, G. (2020). High-Dimensional Index Tracking based on the Adaptive Elastic Net, Quantitative Finance , accepted, (ABS3).Fan, J., Shu, L., Li, Y., and Yang, A. (2020). Phase I Analysis of High-dimensional Covariance Matrices Based on Sparse Leading Eigenvalues, Journal of Quality Technology, accepted.Selected Books and Book ChaptersShu, L., Tsui, K.-L., and Tsung, F., “A Review of Regression Control Charts,” in “Encyclopedia of Statistics in Quality and Reliability,” Ed. Ruggeri, F., Faltin, F., and Kenett, R., Wiley, NY, 1569-1573, 2007.Selected Conference PapersShu, L. and Tsung, F., Multistage Process Monitoring and Diagnosis, Proceedings of the IEEE International Conference on Management of Innovation and Technology, Singapore, 2000.Shu, L. and Tsung, F., Multistage Statistical Quality Control, Proceedings of the Asia-Pacific Conference on Industrial Engineering and Management Systems [CD-ROM], Taipei, Taiwan, 2002.Shu, L., Tsung, F. and Tsui, K.-L., Effects of Estimation Errors on the Performance of Cause-Selecting Charts, INFORMS, Miami, 2002.Shu, L., Tsung, F. and Tsui, K.-L., Run Length Performance of Regression Control Charts with Estimated Parameters, INFORMS, Atlanta, 2003.Shu, L. and Jiang, W., Run length performance of an adaptive CUSUM chart, IFORS, Hawaii, 2005.Shu, L. and Jiang, W., Adaptive CUSUM Procedures with Markovian Mean Estimation, INFORMS, Hong Kong, 2006.Journal of Quality Technology Invited paper presentation, A Weighted CUSUM Chart for Detecting Patterned Mean Shifts, INFORMS, Washington DC, 2008.Shu, L., Jiang, W. and Wu, Z., Markov Chain Approximation to the Performance of Adaptive CUSUM Procedures, Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, 2008.Shu, L., Jiang, W., and Tsui, K.-L.,Weighted CUSUM Procedures for Surveillance of Health Events with Varying Population Sizes, IEEE on IEEM, Macau, 2010.Shu, L., Jiang, W., and Tsui, K.-L.,A Standardized Scan Statistic, the 2nd ICISE, Taiwan, 2012.Huang, W., Shu, L., and Jiang, W., Optimal Design of Cumulative Sum Control Charts under Shift Uncertainty, the 59th Worlds Statistics Congress, Hong Kong, 2013.Shu, L., “A Gradient Approach for Efficient Design of Cusum Charts under Uncertainty, the X-th International Workshop on Intelligent Statistical Quality Control, Sydney, 2013.Zhou, R. and Shu, L., The Test for Detecting Arbitrarily Shaped Spatial Clusters based on Adaptive Minimum Spanning Tree, INFORMS, Minneapolis, 2013.Zhou, R., Shu, L., and Jiang, W., The Weighted Average Likelihood based Tests for Spatial Clusters, IIE Asia, Taipei, 2013.