ESTIMATION OF THE VARIANCE FOR THE MAXIMUM LIKELIHOOD ESTIMATES IN NORMAL MIXTURE MODELS AND NORMAL HIDDEN MARKOV MODELS
ESTIMATION OF THE VARIANCE FOR THE MAXIMUM LIKELIHOOD ESTIMATES IN NORMAL MIXTURE MODELS AND NORMAL HIDDEN MARKOV MODELS
复制标题
正态混合模型和正态隐马尔可夫模型中最大似然估计的方差估计
DOI：
10.5183/jjscs.1002001_183
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
K. Nomakuchi
中科院分区：
文献类型：
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作者：
M. Iqbal;A. Nishi;Yasuki Kikuchi;K. Nomakuchi
本文推导了正态混合模型和正态隐马尔可夫模型的观测信息矩阵。我们还描述了这些模型的参数Bootstrap方法。利用上述矩阵和方法估计期望最大化(EM)算法得到的最大似然估计(MLE)的方差。最后给出了一个数值算例，该算例使用了自由统计软件R中给出的一个名为\FUTIFY的数据集
In this article, we derive the observed information matrices for normal mixture models and normal hidden Markov models. We also describe the parametric bootstrap method for the said models. The matrices and the method mentioned above are used to estimate the variance of the maximum likelihood estimates (MLEs) obtained by the Expectation-Maximization (EM) algorithm. Finally, a numerical example is shown using a data set named \faithful" given in the free statistical software R