參考文獻 |
[1] Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41, 100–115.
[2] Nagaraj, N.K. (1990). Two-sided tests for change in level for correlated data. Statistical
Papers, 31, 181-194.
[3] Pignatiello Jr, J. J. and Samuel, T. R. (2001). Identifying the time of a step-change in the
process fraction nonconforming. Quality Engineering, 13, 357–365.
[4] Perry, M. B. and Pignatiello Jr, J. J. (2005). Estimation of the change point of the process
fraction nonconforming in SPC applications. International Journal of Reliability, Quality
and Safety Engineering, 12, 95-110.
[5] Joe, H. (1997). Multivariate models and multivariate dependence concepts. CRC press,
Florida.
[6] Darsow, W. F., Nguyen, B., and Olsen, E. T. (1992). Copulas and Markov processes. Illinois
journal of mathematics, 36, 600-642.
[7] Long, T. H. and Emura, T. (2014). A control chart using copula-based Markov chain models.
Journal of the Chinese Statistical Association, 52, 466-496.
[8] Sklar, M. (1959). Fonctions de répartition à n dimensions et leurs marges. Annales de
l’ISUP, 8, 229-231.
[9] Chen, X., Wu, W. B., and Yi, Y. (2009). Efficient estimation of copula-based semiparametric
Markov models. The Annals of Statistics, 37, 4214-4253.
[10] Carlin, B. P., Gelfand, A. E., and Smith, A. F. (1992). Hierarchical Bayesian analysis of
changepoint problems. Journal of the royal statistical society: series C (applied statistics),
41, 389-405.
[11] Emura, T., Lai, C. C., and Sun, L. H. (2023). Change point estimation under a copula-based
Markov chain model for binomial time series. Econometrics and Statistics, 28, 120-137.
[12] Liu, L. H. (2021). Change point estimation based on copula-based Markov chain model for
normal time series (Master’s thesis), Department of Statistics, National Central University.
[13] Jian, W. X. (2023). Change point Estimation Based on the Copula-based Markov Chain
Model for Mixture Normal Time Series (Master’s thesis), Department of Statistics, National
Central University.
[14] Nelsen, R.B. (2006). An Introduction to Copulas (2nd ed.). Springer Series in Statistics,
Berlin.
[15] Joe, H. and Zhu, R. (2005). Generalized Poisson distribution: the property of mixture of
Poisson and comparison with negative binomial distribution. Biometrical Journal: Journal
of Mathematical Methods in Biosciences, 47, 219-229.
[16] Clayton, D. G. (1978). A model for association in bivariate life tables and its application
in epidemiological studies of familial tendency in chronic disease incidence. Biometrika,
65, 141-151.
[17] Knight, K. (2000). Mathematical Statistics. Chapman and Hall, New York.
[18] Maguire, B. A., Pearson, E. S., and Wynn, A. H. A. (1952). The time intervals between
industrial accidents. Biometrika, 39, 168-180.
[19] Jarrett, R. G. (1979). A note on the intervals between coal-mining disasters. Biometrika,
66, 191-193.
[20] Worsley, K. J. (1986). Confidence regions and tests for a change-point in a sequence of
exponential family random variables. Biometrika, 73, 91-104.
[21] Cobb, L. (1978). Stochastic catastrophe models and multimodal distributions. Behavioral
Science, 23, 360-374.
[22] Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian
model determination. Biometrika, 82, 711-732.
[23] Lee, S., Park, S., and Chen, C. W. S. (2017) On Fisher’s dispersion test for integer-valued
autoregressive Poisson models with applications. Communications in Statistics - Theory
and Methods, 46, 9985-9994.
[24] Hall, C. A., Snelling, W. O., and Holmes, J. A. (1907). Coal-mine accidents, their causes
and prevention (a preliminary statistical report) , 333, Govt. Print. Off. |