While many methods are available to detect structural changes in a time series, few procedures are available to quantify the uncertainty of these estimates post-detection. In this work, we fill this gap by proposing a new framework to test the null hypothesis that there is no change in mean around an estimated changepoint. We further show that it is possible to efficiently carry out this framework in the case of changepoints estimated by binary segmentation and its variants, $\ell_{0}$ segmentation, or the fused lasso. Our setup allows us to condition on much less information than existing approaches, which yields higher powered tests. We apply our proposals in a simulation study and on a dataset of chromosomal guanine-cytosine content. These approaches are freely available in the R package ChangepointInference at https://jewellsean.github.io/changepoint-inference/.

翻译：虽然在时间序列中有许多方法可以探测结构变化,但很少有程序可以量化这些测出后估计的不确定性。 在这项工作中,我们提出一个新的框架来填补这一空白,以测试一个无效假设,即估计变化点的平均值没有变化。我们进一步表明,在二元分解及其变体估计的变化点的情况下,可以高效地执行这一框架,美元=0美元=9美元=10元=10元=16分解,或引信弧索。我们的设置允许我们以比现有方法少得多的信息为条件,而现有方法能产生更高的动力测试。我们在模拟研究和染色体-抗生素内容数据集中应用我们的建议。这些方法可以在https://jewellsean.github.io/changerpoint-inference/的R包中自由查阅。