Jesús E García, Verónica A González-López, Johsac I Gomez Sanchez
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引用次数: 0
摘要
本文扩展了基于贝叶斯信息准则(BIC)的度量概念,以实现对分区马尔可夫模型(PMM)的强一致性估计。我们从被称为高效判定标准(EDC)的模型选择标准系列中引入了一组度量标准。这一概括扩展了 BIC 中用于惩罚模型参数数量的选项范围。我们正式规定了 EDC 在根据与度量相关的阈值选择模型时的工作关系。此外,我们还改进了 EDC 中的惩罚选项,将 ln(ln(n))惩罚确定为保持 PMM 估计强一致性的可行选择。为了证明这些新指标的实用性,我们将其应用于 2023 年巴西流行的 3 型登革热病毒的三个 DNA 序列建模。
A Metric Based on the Efficient Determination Criterion.
This paper extends the concept of metrics based on the Bayesian information criterion (BIC), to achieve strongly consistent estimation of partition Markov models (PMMs). We introduce a set of metrics drawn from the family of model selection criteria known as efficient determination criteria (EDC). This generalization extends the range of options available in BIC for penalizing the number of model parameters. We formally specify the relationship that determines how EDC works when selecting a model based on a threshold associated with the metric. Furthermore, we improve the penalty options within EDC, identifying the penalty ln(ln(n)) as a viable choice that maintains the strongly consistent estimation of a PMM. To demonstrate the utility of these new metrics, we apply them to the modeling of three DNA sequences of dengue virus type 3, endemic in Brazil in 2023.
期刊介绍:
Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.