We study the maximum score statistic to detect and estimate local signals in the form of change-points in the level, slope, or other property of a sequence of observations, and to segment the sequence when there appear to be multiple changes. We find that when observations are serially dependent, the change-points can lead to upwardly biased estimates of autocorrelations, resulting in a sometimes serious loss of power. Examples involving temperature variations, the level of atmospheric greenhouse gases, suicide rates, incidence of COVID-19, and excess deaths during the pandemic illustrate the general theory.

翻译：我们研究最高评分统计,以测测的层次、坡度或其他属性的变化点的形式,探测和估计当地信号,进行一系列观测,并在出现多重变化时对顺序进行分解;我们发现,如果测算结果依次依存,变化点可能导致对自动关系作出偏向性的估计,有时导致严重丧失权力,包括温度变化、大气温室气体水平、自杀率、COVID-19的发生率以及大流行病期间超额死亡等例子说明了一般理论。