A technique for measuring epidemiologically useful features of birthweight distributions

Birthweight distributions have been conceptualized as a predominant Gaussian distribution contaminated in the tails by an unspecified 'residual' distribution. Acknowledging this idea, we propose a technique for measuring certain features of birthweight distributions useful to epidemiologists: the mean and variance of the predominant distribution; the proportions of births in the low- and high-birthweight residual distributions, and the boundaries of support for these residual distributions. Our technique, based on an underlying multinomial sampling distribution, involves estimating parameters in a mixture model for the multinomial bin probabilities after having chosen the support of the residual distribution with a model selection criterion. A modest simulation study and experience with a few actual datasets indicate that use of a Bayesian information criterion (BIC) as model selection criterion is superior to use of Akaike's information criterion (AIC) in this application.