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Markov chain Monte Carlo test of toric homogeneous Markov chains

机译:马尔可夫链复曲面齐次马尔可夫链的蒙特卡罗检验

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Markov chain models are used in various fields, such as behavioral sciences or econometrics. Although the goodness of fit of the model is usually assessed by large sample approximation, it is desirable to use conditional tests if the sample size is not large. We study Markov bases for performing conditional tests of the toric homogeneous Markov chain model, which is the envelope exponential family for the usual homogeneous Markov chain model. We give a complete description of a Markov basis for the following cases: (i) two-state, arbitrary length, (ii) arbitrary finite state space and length of three. The general case remains to be a conjecture. We also present a numerical example of conditional tests based on our Markov basis.
机译:马尔可夫链模型用于各种领域,例如行为科学或计量经济学。尽管通常通过大样本近似值来评估模型的拟合优度,但如果样本量不大,则希望使用条件测试。我们研究用于执行复曲面齐次马尔可夫链模型的条件测试的马尔可夫基础,这是通常的齐次马尔可夫链模型的包络指数族。对于以下情况,我们给出了马尔可夫基础的完整描述:(i)两种状态,任意长度,(ii)任意有限状态空间,并且长度为3。一般情况仍然是一个推测。我们还提供了一个基于马尔可夫基础的条件测试的数值示例。

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