博碩士論文 109225002 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:55 、訪客IP:3.138.199.50
姓名 張哲豪(Che-Hau Chang)  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卜瓦松序列 數據之線上變化點偵測
(Online Bayesian Changepoint Estimation via the Copula-based Markov Chain Model for Poisson Time Series)
相關論文
★ Credit Risk Illustrated under Coupled diffusions★ The analysis of log returns using copula-based Markov models
★ Systemic risk with relative behavior★ 在厚尾分配下的均值收斂交易策略
★ Comparison of Credit Risk in Coupled Diffusion Model and Merton′s Model★ Estimation in copula-based Markov mixture normal model
★ 金融系統性風險的回顧分析★ New insights on ′′A semi-parametric model for wearable sensor-based physical activity monitoring data with informative device wear"
★ A parametric model for wearable sensor-based physical activity monitoring data with informative device wear★ Optimal Asset Allocation using Black-Litterman with Smooth Transition Model
★ VIX Index Analysis using Copula-Based Markov Chain Models★ 使用雙重指數平滑預測模型及無母數容忍限的配對交易策略
★ Intraday Pairs Trading on Taiwan Semiconductor Companies through Mean Reverting Processes★ Target index tracing through portfolio optimization
★ Estimation in Copula-Based Markov Models under Weibull Distributions★ Optimal Strategies for Index Tracking with Risky Constrains
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2024-9-2以後開放)
摘要(中) 識別序列數據中的變化,稱為改變點偵測,已成為各個領域越來越重要的話
題。改變點偵測法可以分為即時和線下,我們主要針對即時改變點的方法做研究與推廣,稱之為 EXact Online Bayesian Changepoint Detection (EXO),已經對於真實資料顯示出合理的結果。其中,對於資料型態,EXO 假設資料點間是相互獨立的,在真實資料中,資料間其實是有一定的相關性的,對於這種有相關性的資料,我們使用 Clayton copula 之下的馬可夫鏈模型,邊際分配的部分我們使用卜瓦松去描述這種間斷型的資料。從模擬得知在強相關性的情況下,這個模型有較好的準確性。並在實證資料中這個模型與 EXO 方法得到相同的結果。
摘要(英) Detecting the structure change in sequential data, known as changepoint detection,has become increasingly important in various fields. As the changepoint detection method
can be categorized by online and offline, this research focuses on the online way called EXact Online Bayesian Changepoint Detection (EXO). However, EXO assumes that the
datapoints are independent of each other, but this may be unrealistic. For real data, there is a certain relation between the datapoints. Therefore we consider the Markov chain model under the Clayton copula with the Poisson distribution as the marginal distribution to describe the data with the dependence structure and illustrate the performance in simulation studies. The data analysis comes from empirical studies.
關鍵字(中) ★ 改變點
★ Clayton copula
★ 馬可夫鏈模型
★ 貝式推論
★ 卜瓦松分配
關鍵字(英) ★ changepoint
★ Clayton copula
★ Markov model
★ Bayesian Inference
★ Poisson distribution
論文目次 Contents
1 Introduction 1
2 Proposed Model and the Methodology 3
2.1 Copula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Poisson Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Bayesian Online Changepoint Detection . . . . . . . . . . . . . . . . . . . 5
2.4 Bayesian Online Changepoint Detection with the Clayton Copula . . . . . . 9
3 Simulation Study 13
3.1 Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 Empirical Study 19
5 Conclusion 21
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
參考文獻 Adams, R.P. and MacKay, D.J. (2007). Bayesian online changepoint detection. https://
doi.org/10.48550/arXiv.0710.3742.
Aminikhanghahi, S. and Cook, D.J. (2017). A survey of methods for time series change point
detection. Knowledge and Information Systems, 51, 339-367.
Byrd, M., Nghiem, L. and Cao, J. (2017). Lagged exact Bayesian online changepoint detection with parameter estimation. https://doi.org/10.48550/arXiv.1710.03276.
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.
Chib, S. (1998). Estimation and comparison of multiple change-point models. Journal of
econometrics, 86, 221-241.
Chen, X. and Fan, Y. (2006). Estimation and model selection of semiparametric copulabased multivariate dynamic models under copula misspecification. Journal of Econometrics, 135, 125-154.
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,
141–1.
Chen, C.W.S. and Lee, B. (2021). Bayesian inference of multiple structural change models
with asymmetric GARCH errors. Statistical Methods and Applications, 30, 1053-1078.
Darsow, W.F., Nguyen, B. and Olsen, E.T. (1992). Copulas and Markov processes. Illinois
Journal of Mathematics, 36, 600-642.
Domma, F., Giordano, S. and Perri, P.F. (2009). Statistical modeling of temporal dependence
in financial data via a copula function. Communications in Statistics-Simulation and Computation, 38, 703-728.
Emura, T. and Chen, Y.H. (2018). Analysis of survival data with dependent censoring:
Copula-Based Approaches. Springer, Singapore.
Emura, T., 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.
Fearnhead, P. and Liu, Z. (2007). On‐line inference for multiple changepoint problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69, 589-605.
Genest, C. and MacKay, R.J. (1986). Copules archimédiennes et families de lois bidimensionnelles dont les marges sont données. The Canadian Journal of Statistics, 14, 145-159.
Green, P.J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian
model determination. Biometrika, 82, 711-732.
Hastings, W.K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
Joe, H. (1997). Multivariate models and multivariate dependence concepts. CRC press,
Florida.
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.
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 Series in Statistics.
Berlin.
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.
Reeves, J., Chen, J., Wang, X.L., Lund, R. and Lu, Q.Q. (2007). A review and comparison
of changepoint detection techniques for climate data. Journal of Applied Meteorology and
Climatology, 46, 900–915.
Shields, A., Doody, P. and Scully, T. (2017). Application of multiple change point detection
methods to large urban telecommunication networks. 2017 28th Irish Signals and Systems
Conference (ISSC), 1-6.
Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges. Publications de l'
Institut de Statistique de l'Université de Paris, 8, 229–231.
Sun, L.H., Huang, X.W., Alqawba, M.S., Kim, J.M. and Emura, T. (2020). Copula-based
Markov models for time series: Parametric inference and process control. Springer Nature,
Berlin.
指導教授 孫立憲(Li-Hsien Sun) 審核日期 2022-8-20
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明