title: Bayesian approach for a nonlinear growth model.
abstract: Nonlinear least squares methods are currently used for fitting a well-known growth model, namely the Jenss model, to the length measurements of a child followed throughout the first six years of life. An empirical Bayes approach is developed for fitting the model, and the prior distribution of the growth-model parameters is estimated from a large sample of least squares parameters. An expression which is proportional to the posterior distribution is derived so that the posterior mode can be estimated. Given the observations on a child, this posterior mode provides Bayes estimates of the Jenss curve parameters for the child.
mesh_terms: D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D006128:Growth; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008954:Models, Biological; D011336:Probability; D013223:Statistics as Topic