title: A smooth test of goodness-of-fit for growth curves and monotonic nonlinear regression models.
abstract: We propose a goodness-of-fit test for growth curves based on an adaptation of the data-driven smooth test paradigm. It is simple to apply and can assess the fit of a model to a set of growth experiences. A simulation study shows that for small samples, the test holds its level. Moreover, its power is found to be generally greater than existing tests. The article concludes by revisiting the long-standing problem of validating a model for the growth of human stature.
authors.affiliation: Laboratoire de probabilités et statistique, cc051 Université Montpellier II, 34095 Montpellier Cedex 5, France. ducharme@math.univ-montp2.fr
authors.forename: Gilles R
authors.initials: GR
authors.lastname: Ducharme
authors.forename: Bénédicte
authors.initials: B
authors.lastname: Fontez
mesh_terms: D000818:Animals; D001699:Biometry; D001827:Body Height; D003627:Data Interpretation, Statistical; D006128:Growth; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics