References
- Abegaz, F., Naik-Nimbalkar, U. V. (2008). Dynamic copula-based Markov time series. Communications in Statistics –Theory and Method 37:2447–2460.
- Bisgaard, S., Kulahci, M. (2007). Quality quandaries, practical time series modeling II. Quality Engineering 19:393–400.
- Brechmann, E. C., Czado, C. (2014). COPAR-multivariate time series modelling using the copula autoregressive model. Applied Stochastic Models in Business and Industry 31:495–514. DOI: 10.1002/asmb.2043
- Box, G. E. P., Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. 2nd ed. Englewood Cliffs, NJ: Prentice Hall.
- Box, G., Narasimhan, S. (2010). Rethinking statistics for quality control. Quality Engineering 22:60–72.
- Chen, X., Fan, Y. (2006). Estimation of copula-based semiparametric time series models. Journal of Econometrics 130:307–335.
- Darsow, W. F., Nguten, B., Olsen, E. T. (1992). Copulas and Markov processes. Illinois Journal of Mathematics 36:600–642.
- de Uña-Álvarez, J., Veraverbeke, N. (2013). Generalized copula-graphic estimator. Test 22:343–360.
- Domma, F., Giordano, S., Francesco, P. P. (2009). Statistical modeling of temporal dependence in financial data via a copula function. Communications in Statistics - Simulation and Computation 38:703–728.
- Emura, T., Chen, Y. H. (2014). Gene selection for survival data under dependent censoring: A copula-based approach. Statistical Methods in Medical Research. DOI: 10.1177/0962280214533378
- Emura, T., Long, T. H. (2015). R Copula.Markov package: Estimation and Statistical Process Control Under Copula-Based Time Series Models, Version 1.0. Available at: http://cran.r-project.org/web/packages/Copula.Markov/
- Emura, T., Nakatochi, M., Murotani, K., Rondeau, V. (2015). A joint frailty-copula model between tumour progression and death for meta-analysis. Statistical Methods in Medical Research. DOI: 10.1177/0962280215604510
- Hu, Y. H., Emura, T. (2015). Maximum likelihood estimation for a special exponential family under random double-truncation. Computational Statistics 30:1199–1229. DOI 10.1007/s00180-015-0564-z
- Huh, K. (2014). Optimal Monitoring Methods for Univariate and Multivariate EWMA Control Charts. Ph.D. dissertation, McMaster University, Hamilton, Ontario, Canada.
- Hung, Y. C., Tseng, N. F. (2013). Extracting informative variables in the validation of two-group causal relationship. Computational Statistics 28:1151–1167.
- Joe, H. (1993). Parametric families of multivariate distributions with given margins. Journal of Multivariate Analysis 46:262–282.
- Joe, H. (1997). Multivariate Models and Dependence Concepts. Boca Raton, FL: Chapman & Hall/CRC.
- Khuri, A. I. (2003). Advanced Calculus with Applications in Statistics. 2nd ed. New York: Wiley.
- Knoth, S., Schmid, W. (2004). Control charts for time series: A review. In: Lenz, H.-J., Wilrich, P. T., eds. Frontiers in Statistical Quality Control. Vol. 7. Berlin Heidelberg: Springer-Verlag, pp. 210–236.
- Long, T. H., Emura, T. (2014). A control chart using copula-based Markov chain models. Journal of the Chinese Statistical Association 52(4):466–496.
- Montgomery, D. C. (2009). Statistical Quality Control. 6th ed. New York: Wiley.
- Nelsen, R. B. (2006). An Introduction to Copulas (Springer Series in Statistics). 2nd ed. New York: Springer-Verlag.
- Sari, J. K., Newby, M. J., Brombacher, A. C., and Tang, L. C. (2009). Bivariate constant stress degradation model: Led lighting system reliability estimation with two-stage monitoring. Quality and Reliability Engineering International 25:1067–1084.
- Schepsmeier, U., Stöber, J. (2014). Derivatives and Fisher information of bivariate copulas. Statistical Papers 55:525–542.
- Tseng, S.-T., Tang, J., Lin C.-H. (2007). Sample size determination for achieving stability of double multivariate exponentially weighted moving average controller. Technometrics 17:73–80.
- Wieringa, J. E. (1999). Statistical Process Control for Serially Correlated Data. New York: Labyrint Publishing.