Estimating the order of a hidden markov model
Estimating the order of a hidden markov model
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DOI：
10.2307/3316097
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发表时间：
2002-12
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影响因子：
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通讯作者：
Rachel J. Mackay
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文献类型：
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作者：
Rachel J. Mackay
虽然隐马尔可夫模型参数的估计已被广泛研究，但隐藏状态数量的一致估计仍然是一个未解决的问题。 AIC 和 BIC 方法是最常用的，但它们在这种情况下的使用在理论上尚未得到证实。作者表明，对于许多常见模型，惩罚最小距离方法可以对平稳隐马尔可夫模型中的隐藏状态数量进行一致的估计。除了解决可识别性问题之外，她还将她的方法应用于多发性硬化症数据集，并通过模拟评估其性能。
While the estimation of the parameters of a hidden Markov model has been studied extensively, the consistent estimation of the number of hidden states is still an unsolved problem. The AIC and BIC methods are used most commonly, but their use in this context has not been justified theoretically. The author shows that for many common models, the penalized minimum‐distance method yields a consistent estimate of the number of hidden states in a stationary hidden Markov model. In addition to addressing the identifiability issues, she applies her method to a multiple sclerosis data set and assesses its performance via simulation.