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高校应用数学学报
Applied Mathematics A Journal of Chinese Universities  2018, Vol. 33 Issue (2): 157-166    DOI:
    
Bayesian analysis for joint mean and variance models
ZHAO Yuan-ying1, XU Deng-ke2, PANG Yi-cheng3
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Abstract  Bayesian analysis for joint mean and variance models is studied in this paper, in
which Gibbs sampler and Metropolis-Hastings algorithm are used to calculate Bayesian estimations of
unknown parameters and Bayesian case deletion diagnostic. Simulation studies and a real example are
used to illustrate the proposed methodology.

Key words： Bayesian analysis              case deletion              Gibbs sampler       metropolis-Hastings algorithm       joint mean and variance models     
Published: 25 July 2018
CLC:  O212.8  
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ZHAO Yuan-ying
XU Deng-ke
PANG Yi-cheng
Cite this article:

ZHAO Yuan-ying, XU Deng-ke, PANG Yi-cheng. Bayesian analysis for joint mean and variance models. Applied Mathematics A Journal of Chinese Universities, 2018, 33(2): 157-166.

URL:

http://www.zjujournals.com/amjcua/     OR     http://www.zjujournals.com/amjcua/Y2018/V33/I2/157


联合均值与方差模型的Bayes分析

研究联合均值与方差模型的Bayes分析, 通过应用Gibbs抽样和Metropolis-
Hastings(MH)算法计算模型未知参数的Bayes估计与Bayes数据删除影响诊断统计量.
模拟研究和实例分析说明该方法的可行性.

关键词： Bayes 分析 ,   ,  数据删除 ,   Gibbs抽样 ,   MH算法 ,   联合均值与方差模型 
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