博碩士論文 110225020 詳細資訊




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姓名 簡暐勳(Wei-Xun Jian)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 基於 Copula 下的馬可夫鏈模型對於混合常態序 列數據之改變點偵測
(Change point Estimation Based on the Copula-based Markov Chain Model for Mixture Normal Time Series)
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摘要(中) 根據觀察到的序列數據,如果存在一個點將數據從一個特定分佈轉變為另一
個分佈,則該點稱為改變點。改變點檢測可以幫助我們事前警惕和事後分析。
有幾種方法可以檢測改變點。在傳統方法中,最大概似估計法可以幫助我們估
計獨立觀測序列的改變點。然而,在實際數據中,觀測值通常不是獨立的,這
可能導致使用傳統的最大概似估計法獲得準確的改變點估計存在困難。因此,
我們研究了 Clayton copula 下的馬爾可夫鏈,其中邊際分佈是混合正態分佈,以獲得非獨立觀測值下的改變點估計值。此外,模型構建了一個新的最大概似函數,並通過牛頓-拉弗森方法求解參數。通過實證研究來展示所提出方法的性能。
摘要(英) Based on the observed sequential data, if there is structural change at some particular time, this critical point of structural change is the change point. The change point detection helps in both proactive warning and retrospective analysis. In real applications, observations are often not independent. Therefore, we propose the Clayton copula based the Markov chain which the marginal distribution is the mixture normal distribution to estimate the change point for the time dependent observations. Consequently, the corresponding maximum likelihood function is obtained and the maximum likelihood estimates are solved through the Newton-Raphon method. The performance for the proposed method is illustrated using simulation studies. Finally, we discuss the structural change in the real application based on two periods: 2008 financial crisis and 2020 COVID-19.
關鍵字(中) ★ 改變點
★ 馬可夫鏈模型
★ 牛頓法
★ 混和常態分配
關鍵字(英) ★ change point
★ Markov model
★ Newton-Raphon method
★ mixture normal distribution
論文目次 Contents
1 Introduction ... 1
2 Proposed Model and the Methodology ... 3
2.1 Clayton Copula and the Marginal Distribution ... 3
2.2 Change Point Detection ... 4
2.3 The Implementation of the Newton-Raphson(NR) method ... 6
3 Simulation ... 9
3.1 Setting ... 9
3.2 Simulation Results ... 10
3.3 Model Misspecification ... 11
4 Empirical Study ... 24
5 Conclusion ... 31
參考文獻 Chen, X., Wu, W.B., and Yi, Y. (2009). Efficient estimation of copula-based semiparametric Markov models. The Annals of Statistics, 37, 4214–4253.
Clayton, D.G. (1978). A model for association in bivariate lifetables and its application in
epidemiological studies of familial tendency in chronic disease incidence. Biometrika, 65, 141–151.
Darsow, W.F., Nguyen, B., and Olsen, E.T. (1992). Copulas and Markov processes. Illinois Journal of Mathematics, 36, 600-642.
Jarque, C.M., and Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6, 255-259. https://doi.org/10.1016/0165-1765(80)90024-5.
Joe, H. (1997). Multivariate models and multivariate dependence concepts. CRC press,Florida.
Lavielle, M. and Teyssiere, G. (2007). Adaptive detection of multiple change-points in asset price volatility. Long Memory in Economics, 129-156.
Lin, W.C. (2018). Estimation in copula-based Markov mixture normal model (Master's thesis), Department of Statistics, National Central University. https://hdl.handle.net/
11296/9gb878.
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. https://hdl.handle.net/11296/a8batc.
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.
MacDonald, I.L. (2014). Does Newton–Raphson really fail? Statistical Methods in Medical Research. 23, 308-311.
Nagaraj, N.K. (1990). Two-sided tests for change in level for correlated data. Statistical Papers, 31, 181-194.
Nelsen, R.B. (2006). An Introduction to Copulas(2nd ed.). Springer Science Business Media, New York.
Page, E.S. (1954). Continuous inspection schemes. Biometrika, 41, 100-115.
Pearson, K. (1894). Contributions to the mathematical theory of evolution. Philosophical Transactions of the Royal Society Series A. 185, 71-110.
Perry, M.B. and Pignatiello, J.J.Jr. (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.
Pignatiello, J.J. and Samuel, T.R. (2000). Identifying the time of a step change in the process fraction nonconforming. Quality Engineering, 13, 357-365.
Prado R., Ferreira M. and West M. (2021). Time Series: Modeling, Computation, and Inference (2nd ed.). CRC press.
Reeves, J., Chen, J., Wang, X.L., Lund, R., and Lu, Q.Q. (2007). A review and comparison of change point detection techniques for climate data. Journal of Applied Meteorology and Climatology, 46, 900–915.
Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publications de l'Institutde Statistique de l'Université de Paris. 8, 229–231.
Takeshi E., Lai, C.C. and Sun, L.H. (2021). Change point estimation under a copula-based Markov chain model for binomial time series. Econometrics and Statistics. https://doi.org/10.1016/j.ecosta.2021.07.007.
指導教授 孫立憲(Li-Hsien Sun) 審核日期 2023-7-21
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