Application of the bootstrap method for change points analysis in generalized linear models
Application of the bootstrap method for change points analysis in generalized linear models
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DOI：
10.1007/s42081-018-0023-5
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发表时间：
2018-10
影响因子：
1.3
通讯作者：
Asanao Shimokawa;Etsuo Miyaoka
中科院分区：
文献类型：
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作者：
Asanao Shimokawa;Etsuo Miyaoka
本文重点研究了分段不同系数广义线性模型的预测模型的构造方法、变点位置的估计方法以及预测模型的可信区间。层次分裂算法作为多变点分析的标准方法，得到了广泛的应用。然而，分层分裂算法具有很高的风险，因为变点估计器的标准误差变大，从而降低了估计模型的预测精度。为了解决这个问题，我们考虑了一种基于分层分裂算法的Bootstrap方法的应用。通过仿真研究，从估计模型的预测精度、变点估计器的偏差和方差、变点的可信区间精度等方面对算法进行了比较。从结果中，我们证实了基于Bootstrap的方法在变点分析中的实用性，特别是提高了模型的预测精度，降低了变点估计器的标准误差，并根据情况构建了更好的置信度区间。文中还给出了一个简单算例的结果，以说明该方法的实用性。
In this paper, we focus on the construction methods of the prediction model, estimation methods of the change point locations, and the confidence intervals for the generalized linear model with piecewise different coefficients. As a standard approach for multiple change point analysis, the application of the hierarchical splitting algorithm is widely used. However, the hierarchical splitting algorithm has a high risk in that the standard error of the change point estimators become large and, therefore, the prediction accuracy of the estimated model decreases. To deal with this problem, we consider the application of a bootstrap method based on the hierarchical splitting algorithm. Through simulation studies, we compare the algorithms in terms of the prediction accuracy of the estimated model, bias and variance of the change point estimators, and the accuracy of the confidence intervals of the change points. From the result, we confirmed the utility of the bootstrap-based methods for change point analysis, especially the increased prediction accuracy of the obtained model, decreased standard error of the change point estimators, and construction of better confidence intervals depending on the situation. We also present the results of a simple example to demonstrate the utility of the method.