Lampl M, Johnson M L
Emory University, Atlanta, GA, USA.
Ann Hum Biol. 1998 May-Jun;25(3):187-202. doi: 10.1080/03014469800005572.
This paper elucidates the effects of moving average filters when applied to serial growth measurements. This is a question of interest because smoothing procedures are inherently part of a number of analytical methods presently employed in auxological analyses. Particular attention is paid to sequential growth data analysed to identify what has been described as pulsatile, saltation and stasis patterns or mini-growth spurts. When applied to pulsatile, or saltatory, time series data the process of smoothing itself creates artifactual temporal patterns in the time series data similar to previously described mini growth spurts while removing the actual pulsatile characteristics of the data. These observations illustrate that smoothing approaches add noise to time series data while removing meaningful patterns in the original data sequence. Analyses employing such approaches produce results that include waveforms or other fluctuations compatible with an underlying pulsatile driving mechanism, but do not necessarily reflect the temporal characteristics of the original biological process.
本文阐明了移动平均滤波器应用于连续生长测量时的效果。这是一个值得关注的问题，因为平滑程序是目前许多人体测量学分析方法中固有的一部分。特别关注对连续生长数据进行分析，以识别所谓的脉动、跳跃和停滞模式或微小生长突增。当应用于脉动或跳跃时间序列数据时，平滑过程本身会在时间序列数据中产生与先前描述的微小生长突增类似的人为时间模式，同时消除数据的实际脉动特征。这些观察结果表明，平滑方法在去除原始数据序列中有意义的模式的同时，还给时间序列数据添加了噪声。采用这种方法的分析产生的结果包括与潜在脉动驱动机制相符的波形或其他波动，但不一定反映原始生物过程的时间特征。
