Modeling birthweight and gestational age distributions: additive vs. multiplicative processes.

Researchers have traditionally employed Gaussian distributions to model quantitative biological traits. Recently, mixtures of Gaussian distributions have begun to be used as well. However, there are many alternatives to the Gaussian distribution. From a theoretical perspective, the lognormal distribution is as applicable as the Gaussian (both are justified on the basis of the Central Limit Theorem). Here, the utility of mixtures of Gaussians and lognormals for describing birthweight and gestational age distributions are compared. This is carried out within the context of the hybrid-lognormal distribution, in which the Gaussian and lognormal are special cases. The data consists of African American births (1985-1988) and European American births (1988) in the state of New York. The results suggest that of the conventional distributions, a mixture of two Gaussians generally provides the best fit to birthweight and gestational age. However, in the case of birthweight a two-component hybrid-lognormal fits better than any of the simpler models. This may be due to a feature of the hybrid-lognormal distribution that can be interpreted as maternal constraints on fetal development. Copyright 2002 Wiley-Liss, Inc.