SFB 303 Discussion Paper No. A - 367

Author: Kneip, Alois, and Joachim Engel
Title: Model Estimation in Nonlinear Regression under Shape Invariance
Abstract: Given data from a sample of noisy curves we consider a nonlinear parametric regression model with unknown model function. An iterative algorithm for estimating individual parameters as well as the model function is introduced under the assumption of a certain shape invariance: the individual regression curves are obtained from a common shape function by linear transformations of the axes. Our algorithm is based on least-squares methods for parameter estimation and on nonparametric kernel methods for curve estimation. Asymptotic distributions are derived for the individual parameter estimators as well as for the estimator of the shape function. An application to human growth data illustrates the method.
Keywords: Model Selection, Samples of Curves, Nonparametric Smoothing, Semiparametric Methods, Kernel Estimators, Human Growth Analysis
JEL-Classification-Number:
Creation-Date: February 1992
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