Letter to the editors

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.

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Letter to the editors

Cited by 3 publications

References 9 publications

Distribution methods and analysis of nonlinear longitudinal data

Distribution methods and analysis of nonlinear longitudinal data

Distinguishing models of growth with approximate entropy

Wrinkles induced by the use of smoothing procedures applied to serial growth data

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