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A TECHNIQUE FOR MEASURING EPIDEMIOLOGICALLY USEFUL FEATURES OF BIRTHWEIGHT DISTRIBUTIONS
Author(s) -
UMBACH DAVID M.,
WILCOX ALLEN J.
Publication year - 1996
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19960715)15:13<1333::aid-sim271>3.0.co;2-r
Subject(s) - akaike information criterion , bayesian information criterion , statistics , multinomial distribution , residual , poisson distribution , deviance information criterion , selection (genetic algorithm) , mathematics , model selection , variance (accounting) , bayesian probability , computer science , bayesian inference , artificial intelligence , algorithm , accounting , business
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.