Bootstrapping sequential change-point tests for linear regression
Bootstrapping sequential change-point tests for linear regression
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DOI：
10.1007/s00184-011-0347-7
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发表时间：
2012-07
期刊：
影响因子：
0.7
通讯作者：
M. Hušková;C. Kirch
中科院分区：
文献类型：
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作者：
M. Hušková;C. Kirch
提出了线性回归模型中序列变化点检测程序的自举方法。设计相应的监测程序来控制总体显著性水平。通过包括从监测中获得的新观测值，引导临界值不断更新。研究了这些序列自举过程的理论性质，证明了它们的渐近有效性。在模拟研究中比较了自举法和渐近法，表明学生自举法在具有相当的功率和运行长度的同时更好地保持整体水平，特别是对于较小的历史样本量。
Bootstrap methods for sequential change-point detection procedures in linear regression models are proposed. The corresponding monitoring procedures are designed to control the overall significance level. The bootstrap critical values are updated constantly by including new observations obtained from the monitoring. The theoretical properties of these sequential bootstrap procedures are investigated, showing their asymptotic validity. Bootstrap and asymptotic methods are compared in a simulation study, showing that the studentized bootstrap tests hold the overall level better especially for small historic sample sizes while having a comparable power and run length.