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基于SA-MCMC算法的非线性测量误差模型的影响分析 被引量：1

Local influence analysis for nonlinear measurement error models based on the SA-MCMC algorithm
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摘要 研究了非线性测量误差模型的影响分析.首先把模型中有误差的不可观测的数据当作是缺失数据,接着用SA-MCMC算法得到了模型参数的最大似然估计,然后考虑用Q函数代替可观测数据的对数似然函数来进行影响分析,得到了建立在Q函数上的局部影响分析的诊断统计量.最后用具体的例子说明了诊断统计量的有效性. This paper studies the influence analysis for nonlinear measurement error models. We treat the unobservable measurement errors as missing data. The maximum likelihood estimates are obtained by stochastic approximation algorithm with Markov Chain Monte Carlo （SA- MCMC） method. We replace the observable-data log-likelihood function with Q-function. Then, local influence measures are derived based on the Q-function. A real example is given to illustrate the usefulness of diagnostic measures.
作者 徐亮 李艳 周明华 林金官
机构地区 东南大学数学系 浙江工业大学理学院
出处 《浙江工业大学学报》 CAS 2008年第6期693-698,共6页 Journal of Zhejiang University of Technology
基金 江苏省自然科学基金资助项目(BK2008284) 东南大学校基金(9207011430)
关键词 缺失数据 MH算法 SA-MCMC算法 Q函数 局部影响分析 missing-data MH algorithm SA-MCMC algorithm Q-function local influence analysis
分类号 O212.2 [理学—概率论与数理统计]
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参考文献18

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二级参考文献11

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浙江工业大学学报
2008年 第6期

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