ABSTRACT An approach is presented that combines a statistical test for peak detection with the estimation of peak positions in time series. Motivated by empirical observations in neuronal recordings, we aim at investigating peaks of different heights and widths. We use a moving window approach to compare the differences of estimated slope coefficients of local regression models. We combine multiple windows and use the global maximum of all different processes as a test statistic. After rejection, a multiple filter algorithm combines peak positions estimated from multiple windows. Analysing neuronal activity recorded in anaesthetized mice, the procedure could identify significant differences between two brain states concerning peak occurrences and intermediate down states showing no peaks. This suggests that the method can be useful in the analysis of time series showing variability of peak shapes. The method is implemented in the -package (available on CRAN).

中文翻译：

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一种用于测试和检测时间序列峰值的多尺度方法

摘要 提出了一种将峰值检测的统计测试与时间序列中峰值位置估计相结合的方法。受神经元记录中经验观察的启发，我们的目标是调查不同高度和宽度的峰值。我们使用移动窗口方法来比较局部回归模型的估计斜率系数的差异。我们组合多个窗口，并使用所有不同进程的全局最大值作为测试统计量。拒绝后，多滤波器算法组合从多个窗口估计的峰值位置。分析麻醉小鼠中记录的神经元活动，该过程可以确定两种大脑状态之间的显着差异，即峰值发生率和中间下降状态没有显示峰值。这表明该方法可用于分析显示峰形可变性的时间序列。该方法在 -package（在 CRAN 上可用）中实现。