Hidden Markov models for time series of counts with excess zeros
Hidden Markov models for time series of counts with excess zeros
复制标题
用于具有多余零的计数时间序列的隐马尔可夫模型
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
James Ridgway
中科院分区：
文献类型：
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作者：
Madalina Olteanu;James Ridgway
整数值时间序列通常使用马尔可夫模型或隐马尔可夫模型 (HMM) 进行建模。然而，当系列表示计数数据时，通常会出现多余的零。在这种情况下，通常的分布（例如二项分布或泊松分布）无法正确估计零质量。为了克服这个问题，我们在隐马尔可夫模型中引入零膨胀分布。模拟数据和实际数据的实证结果显示出良好的收敛特性，而多余零点的估计效果比经典 HMM 更好。
Integer-valued time series are often modeled with Markov models or hidden Markov models (HMM). However, when the series represents count data it is often subject to excess zeros. In this case, usual distributions such as binomial or Poisson are unable to estimate the zero mass correctly. In order to overcome this issue, we introduce zero-inflated distributions in the hidden Markov model. The empirical results on simulated and real data show good convergence properties, while excess zeros are better estimated than with classical HMM.