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Empirical Likelihood Statistical Inference for Compound Poisson Vector Processes under Infinite Covariance Matrix
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作者 程从华 《Journal of Donghua University(English Edition)》 CAS 2023年第1期122-126,共5页
The paper discusses the statistical inference problem of the compound Poisson vector process(CPVP)in the domain of attraction of normal law but with infinite covariance matrix.The empirical likelihood(EL)method to con... The paper discusses the statistical inference problem of the compound Poisson vector process(CPVP)in the domain of attraction of normal law but with infinite covariance matrix.The empirical likelihood(EL)method to construct confidence regions for the mean vector has been proposed.It is a generalization from the finite second-order moments to the infinite second-order moments in the domain of attraction of normal law.The log-empirical likelihood ratio statistic for the average number of the CPVP converges to F distribution in distribution when the population is in the domain of attraction of normal law but has infinite covariance matrix.Some simulation results are proposed to illustrate the method of the paper. 展开更多
关键词 compound poisson vector process(CPVP) infinite covariance matrix domain of attraction of normal law empirical likelihood(EL)
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基于经验似然方法的向量复合泊松过程研究(英文) 被引量:1
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作者 程从华 《应用数学》 CSCD 北大核心 2018年第2期408-416,共9页
在本文中,在随机过程的一阶矩和二阶矩都存在的条件下,当随机过程时间趋于无穷大时,我们将证明关于向量复合泊松过程期望的对数似然比统计量依分布收敛到F分布.最后,通过数值模拟实验表明我们的方法是可行的.
关键词 向量复合泊松过程 置信域 经验似然
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