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具有稳定分布噪声的多重季节模型的贝叶斯分析

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具有稳定分布噪声的多重季节模型的贝叶斯分析 王红军1, 汤银才21. 西安电子科技大学数学与统计学院, 西安 710071;
2. 华东师范大学统计学院, 上海 200241 Bayesian Inference for Multiplicative Seasonal Models with Stable Innovations WANG Hongjun1, TANG Yincai21. School of Mathematics and Statistics, Xidian University, Xi'an 710071, China;
2. School of Statistics, East China Normal University, Shanghai 200241, China
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摘要本文研究了具有稳定分布噪声的多重季节时间序列模型的建模及应用.稳定分布能够描述诸如方差无限、厚尾、有偏等非正态特征,但该类分布通常没有解析的密度函数,且参数的后验分布比较复杂.本文采用基于抽样的MCMC方法和切片抽样法估计模型参数,将多重季节模型的回归参数和稳定分布中的参数一起估计.通过模拟分析,说明了稳定分布的一些统计性质和文中建模方法的有效性.将模型应用于一个具有季节性和厚尾特征的实际数据集,演示了该类模型的应用价值.
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收稿日期: 2016-10-21
PACS:TP391
N93
基金资助:国家自然基金(11271136,81530086);高等学校学科创新引智计划(B14019)以及基本科研业务费(JB150707)资助项目.
引用本文:
王红军, 汤银才. 具有稳定分布噪声的多重季节模型的贝叶斯分析[J]. 应用数学学报, 2017, 40(4): 519-529. WANG Hongjun, TANG Yincai. Bayesian Inference for Multiplicative Seasonal Models with Stable Innovations. Acta Mathematicae Applicatae Sinica, 2017, 40(4): 519-529.
链接本文:
http://123.57.41.99/jweb_yysxxb/CN/ http://123.57.41.99/jweb_yysxxb/CN/Y2017/V40/I4/519


[1] Fama E F. The behaviour of stock market prices. Journal of Business, 1965, 38:35-105
[2] Nolan J P. Stable distributions models for heavy tailed data. Working paper, 2004
[3] Marco L. Simulation-based Estimation Methods for α-stable Distributions and Progresses. Ph. D Thesis, 2004
[4] Buckle D J. Bayesian inference for stable distributions. Journal of the American Statistical Association, 1995, 90(430):605-613
[5] Qiou Z, Ravishanker N. Bayesian inference for time series with stable distributions. Journal of Time Series Analysis, 1996, 19(2):235-249
[6] Wang H J, Tang Y C. Bayesian Inference for ARMA model with Stable Innovations, and Applications. Acta Mathematicae Applicatae Sinica, 2015, 38(3):466-476
[7] Li H F, Tang Y C. Bayesian Statistical Analysis of Accelerated Life Tests for Log-stable Distribution. J. Sys. Sci. & Math. Scis., 2011, 31(4):448-457
[8] Box G E P, Jenkins G M, Reinsel G C. Time Series Analysis:Forecasting and Control, 3nd edn. Beijing:Posts & Telecom Press, 1994
[9] Brock P J, Davis R A.(2009) Introduction to Time Series and Forecasting, 2nd ed. Beijing:Posts & Telecom Press, 2009
[10] Hastings W K. Monte carlo sampling methods using Markov chains, and their applications. Biometrika, 1970, 57:97-109
[11] 茆诗松, 王静龙, 濮晓龙. 高等数理统计. 北京:高等教育出版社; 海德堡, 柏林:施普林格出版社, 2000(Mao S S, Wang J L, Pu X L. Advanced Mathematical Statistics. Beijing China Higher Education Press; Heidelberg, Berlin:Springer-Verlag, 2000)
[12] Neal, R M. Slice sampling. The Annals of Statistics, 2003, 31(3):705-741
[13] Berger J. Statistical Decision Theory and Bayesian Analysis, Second Edition R[M]. New York, Berlin, Heidelberg:Springer-Verlag, 1990
[14] 韦来生, 张伟平. 贝叶斯分析. 合肥:中国科学技术大学出版社, 2013(Wei L S, Zhang W P. Bayesian Analysis. Hefei:University of Science and Technology of China Press, 2013)
[15] 蔡瑞胸著, 李洪成等译. 金融数据分析导论-基于R语言. 北京:机械工业出版社, 2013(Tsay R S. An Introduction to Analysis of Financial Data with R[M]. Beijing:China Machine Press, 2013)
[16] Cryer J D, Chan K S著, 潘虹宇等译. 时间序列分析及应用-R语言. 北京:机械工业出版社, 2013(Cryer J D, Chan K S. Time Series Analysis with Applications in R[M]. Beijing:China Machine Press, 2013)
[17] 茆诗松, 汤银才. 贝叶斯统计. 北京:中国统计出版社, 2012(Mao S S, Tang Y C. Bayesian Statistics. Beijing:China Statistics Press, 2012)
[18] 吴喜之, 刘苗. 应用时间序列分析-R软件陪同. 北京:机械工业出版社, 2014(Wu X Z, Liu M. Applied Time Series Analysis with R[M]. Beijing:China Machine Press, 2014)
[19] 何书元. 应用时间序列分析. 北京:北京大学出版社, 2007(He S Y. Applied Time Series. Beijing:Peking University Press, 2007)

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