Open Access
Volume 40, Number 2, April 2022
Page(s) 288 - 295
Published online 03 June 2022
  1. Li C I, Pan J N, Huang M H. A new demerit control chart for monitoring the quality of multivariate Poisson processes[J]. Journal of Applied Statistics, 2019, 46(4): 680–699 [Article] [CrossRef] [Google Scholar]
  2. Pascual F G, Akhundjanov S B. Copula-based control charts for monitoring multivariate Poisson processes with applica-tion to hepatitis C counts[J]. Journal of Quality Technology, 2020, 52(2): 128–144 [Article] [CrossRef] [Google Scholar]
  3. Chiu J E, Kuo T I. Attribute control chart for multivariate Poisson distribution[J]. Communications in Statistics-Theory and Methods, 2007, 37(1): 146–158 [Article] [CrossRef] [Google Scholar]
  4. Lowry C A, Woodall W H, Champ C W, et al. A multivariate exponentially weighted moving average control chart[J]. Technometrics, 1992, 34(1): 46 [Article] [CrossRef] [Google Scholar]
  5. Niaki S T A, Abbasi B. Monitoring multi-attribute processes based on norta inverse transformed vectors[J]. Communications in Statistics-Theory and Methods, 2009, 38(7): 964–979 [Article] [CrossRef] [Google Scholar]
  6. Chen N, Li Z, Ou Y. Multivariate exponentially weighted moving-average chart for monitoring Poisson observations[J]. Journal of Quality Technology, 2015, 47(3): 252–263 [Article] [CrossRef] [Google Scholar]
  7. He S, He Z, Wang G A. CUSUM control charts for multivariate Poisson distribution[J]. Communications in Statistics-Theory and Methods, 2014, 43(6): 1192–1208 [Article] [CrossRef] [Google Scholar]
  8. Long Wei, Li Yanting. CUSUM control chart design for multivariate Poisson distribution[J]. Chinese Journal of Applied Probability and Statistics, 2020(3): 221–237 [Article] (in Chinese) [Google Scholar]
  9. Dokouhaki P, Noorossana R. A copula Markov CUSUM chart for monitoring the bivariate auto-correlated binary observations[J]. Quality and Reliability Engineering International, 2013, 29(6): 911–919 [Article] [CrossRef] [Google Scholar]
  10. Sasiwannapong S, Sukparungsee S, Busababodhin P, et al. The efficiency of constructed bivariate copulas for MEWMA and hotelling's T2 control charts[J]. Communications in Statistics-Simulation and Computation, 2022, 51(4): 1837–1851 [Article] [CrossRef] [Google Scholar]
  11. Sukparungsee S, Kuvattana S, Busababodhin P, et al. Bivariate copulas on the hotelling's T2 control chart[J]. Communications in Statistics-Simulation and Computation, 2018, 47(2): 413–419 [Article] [CrossRef] [Google Scholar]
  12. Kuvattana S, Sukparungsee S. Comparative the performance of control charts based on copulas[C]//World Congress on Engineering and Computer Science, 2017: 47–58 [Google Scholar]
  13. Fatahi A A, Noorossana R, Dokouhaki P, et al. Copula-based bivariate ZIP control chart for monitoring rare events[J]. Communications in Statistics-Theory and Methods, 2012, 41(15): 2699–2716 [Article] [CrossRef] [Google Scholar]
  14. Krupskii P, Harrou F, Hering A S, et al. Copula-based monitoring schemes for non-Gaussian multivariate processes[J]. Journal of Quality Technology, 2020, 52(3): 219–234 [Article] [CrossRef] [Google Scholar]
  15. Chen Y. EWMA control charts for multivariate autocorrelated processes[J]. Statistics and Its Interface, 2017, 10(4): 575–584 [Article] [CrossRef] [Google Scholar]
  16. Song Z, Mukherjee A, Zhang J. Some robust approaches based on copula for monitoring bivariate processes and component-wise assessment[J]. European Journal of Operational Research, 2021, 289(1): 177–196 [Article] [CrossRef] [Google Scholar]
  17. Clayton D G. A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence[J]. Biometrika, 1978, 65(1): 141–151 [Article] [CrossRef] [Google Scholar]
  18. Zimmer D M, Trivedi P K. Using trivariate copulas to model sample selection and treatment effects[J]. Journal of Business & Economic Statistics, 2006, 24(1): 63–76 [CrossRef] [Google Scholar]
  19. Ferreira P H, Louzada F. Extending the inference function for augmented margins method to implement trivariate Clayton copula-based SUR tobit models[J]. Communications in Statistics-Theory and Methods, 2020, 49(6): 1375–1401 [Article] [CrossRef] [Google Scholar]
  20. Joe H. Asymptotic efficiency of the two-stage estimation method for copula-based models[J]. Journal of Multivariate Analysis, 2005, 94(2): 401–419 [Article] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.