Fisheries Resources Institute, Japan Fisheries Research and Education Agency, 2-12-4 Fukuura, Kanazawa, Yokohama, Kanagawa, 236-8648, Japan.
The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan.
Sci Rep. 2021 Jun 7;11(1):11938. doi: 10.1038/s41598-021-91327-8.
Nonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner-recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.
非线性现象在生态学中普遍存在。然而，由于自相关性和异常值的存在，它们的推断和预测通常较为困难。传统的最小二乘法参数估计方法能够通过估计自相关性来提高短期预测的准确性，但是对于异常值的处理能力较弱，因此长期预测的效果较差。相比之下，传统的稳健回归方法，如最小绝对偏差法，可以减轻异常值的影响，具有潜在的更好的长期预测能力，但准确估计自相关性较为困难，可能导致短期预测效果较差。我们提出了一种新的稳健回归方法，该方法能够准确地估计自相关性并减少异常值的影响。然后，我们使用模拟数据和真实生态学数据将新方法与传统的最小二乘法和最小绝对偏差法进行比较。模拟和真实数据分析表明，新方法在使用产卵-补充数据进行非线性估计问题时，通常具有更好的长期和短期预测能力。即使对于存在极端异常值的高度污染模拟数据，新方法也能提供几乎无偏的自相关估计，而其他方法则无法准确估计自相关性。
