Cited by

• 2020Phase-I monitoring of high-dimensional covariance matrix using an adaptive thresholding LASSO rule34 cited
• 2017Simultaneously monitoring frequency and magnitude of events based on bivariate gamma distribution29 cited
• 2020A review of dispersion control charts for multivariate individual observations28 cited
• 2016A Robust Multivariate EWMA Control Chart for Detecting Sparse Mean Shifts27 cited
• 2018A distribution‐free control chart for monitoring high‐dimensional processes based on interpoint distances27 cited
• 2017Exponentially weighted control charts to monitor multivariate process variability for high dimensions23 cited
• 2017A CUSUM chart for detecting the intensity ratio of negative events22 cited
• 2017A robust multivariate sign control chart for detecting shifts in covariance matrix under the elliptical directions distributions21 cited
• 2019An adaptive thresholding-based process variability monitoring21 cited
• 2020Phase I analysis of high-dimensional covariance matrices based on sparse leading eigenvalues20 cited
• 2016A control scheme for autocorrelated bivariate binomial data18 cited
• 2017Monitoring multivariate process variability via eigenvalues18 cited
• 2021A new multivariate CUSUM chart for monitoring of covariance matrix with individual observations under estimated parameter17 cited
• 2018A control scheme for monitoring process covariance matrices with more variables than observations16 cited
• 2020On the Efficient Monitoring of Multivariate Processes with Unknown Parameters15 cited
• 2021Advanced multivariate cumulative sum control charts based on principal component method with application15 cited