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摘要翻译：
研究了满足一定混合条件的均值和相依误差突变的AMOC时间序列模型。通过bootstrapping方法获得未知变点的置信区间。精确地说，我们使用估计的中心错误序列的块引导。然后，我们用与前面相同的估计量重建一个均值变化的序列。重采样序列的变点估计量与原始序列的变点估计量之差可以作为真实变点与其估计量之差的近似。这使得我们能够利用重采样时间序列的经验分布来构造置信区间。仿真研究表明，重采样的置信区间通常更接近目标水平，同时小于渐近区间。
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英文标题：
《Bootstrapping confidence intervals for the change-point of time series》
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作者：
Marie Huskova, Claudia Kirch
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最新提交年份：
2007
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分类信息：
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
We study an AMOC time series model with an abrupt change in the mean and dependent errors that fulfill certain mixing conditions. We obtain confidence intervals for the unknown change-point via bootstrapping methods. Precisely we use a block bootstrap of the estimated centered error sequence. Then we reconstruct a sequence with a change in the mean using the same estimators as before. The difference between the change-point estimator of the resampled sequence and the one for the original sequence can be use as an approximation of the difference between the real change-point and its estimator. This enables us to construct confidence intervals using the empirical distribution of the resampled time series. A simulation study shows that the resampled confidence intervals are usually closer to their target levels and at the same time smaller than the asymptotic intervals.
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PDF链接：
https://arxiv.org/pdf/706.1485
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