Abidi H, Borms J, Duquet W, Pontier J
Centre Hospitalier Lyon-Sud, Pierre Bénite, France.
Growth Dev Aging. 1996 Autumn-Winter;60(3-4):113-29.
The estimation of the parameters of a nonlinear model by means of the maximum likelihood procedure is widely used in the study of growth phenomena. The accuracy with which these parameters are calculated is a function of the number of measures taken and particularly, of their distribution across the growth period. If the growth curve is only partially known, the inaccuracy can increase considerably. However, if we have information on the distribution of the parameters of a model in the population, the empirical Bayes method should be used. In this paper, the principle of this approach for nonlinear modeling was recalled. The method was then applied on data of human height. Four nonlinear models are used and their performances are compared. The results show the importance of information on the quality of estimates of growth parameters and consequently on the prediction of adult height.
通过最大似然法估计非线性模型的参数在生长现象研究中被广泛应用。这些参数的计算精度是测量次数的函数，尤其是测量次数在整个生长周期中的分布情况。如果生长曲线只是部分已知，不准确性可能会大幅增加。然而，如果我们掌握了模型参数在总体中的分布信息，就应该使用经验贝叶斯方法。本文回顾了这种非线性建模方法的原理。然后将该方法应用于人类身高数据。使用了四个非线性模型并比较了它们的性能。结果表明了关于生长参数估计质量的信息的重要性，进而也表明了其对成年身高预测的重要性。
