Detecting change-points in Markov chains
Detecting change-points in Markov chains
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DOI：
10.1016/j.csda.2006.11.040
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发表时间：
2007-08-15
影响因子：
1.8
通讯作者：
Polansky, Alan M.
中科院分区：
文献类型：
--
作者：
Polansky, Alan M.
马尔可夫链为相关随机变量提供了灵活的模型，可应用于物理学、环境科学和经济学等学科。在马尔可夫链的应用研究中，评估转移概率矩阵在观察到的过程实现过程中是否发生变化可能很有趣。如果发生这样的变化，估计变化发生的转变以及每次变化之前和之后的概率转变矩阵将是有意义的。对于已知变化数量的情况，标准似然理论被开发来解决这个问题。 Bootstrap 用于帮助计算 p 值。当变化数量未知时，AIC和BIC度量用于模型选择。所提出的方法经过实证研究并应用于示例数据集。 (C) 2007 Elsevier B.V. 保留所有权利。
Markov chains provide a flexible model for dependent random variables with applications in such disciplines as physics, environmental science and economics. In the applied study of Markov chains, it may be of interest to assess whether the transition probability matrix changes during an observed realization of the process. If such changes occur, it would be of interest to estimate the transitions where the changes take place and the probability transition matrix before and after each change. For the case when the number of changes is known, standard likelihood theory is developed to address this problem. The bootstrap is used to aid in the computation of p-values. When the number of changes is unknown, the AIC and BIC measures are used for model selection. The proposed methods are studied empirically and are applied to example sets of data. (C) 2007 Elsevier B.V. All rights reserved.