The Annals of Statistics

Model Estimation in Nonlinear Regression Under Shape Invariance

Alois Kneip and Joachim Engel

Full-text: Open access

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.

Article information

Source
Ann. Statist., Volume 23, Number 2 (1995), 551-570.

Dates
First available in Project Euclid: 11 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176324535

Digital Object Identifier
doi:10.1214/aos/1176324535

Mathematical Reviews number (MathSciNet)
MR1332581

Zentralblatt MATH identifier
0828.62052

JSTOR
links.jstor.org

Subjects
Primary: 62J02: General nonlinear regression
Secondary: 62G07: Density estimation

Keywords
Model selection samples of curves nonparametric smoothing semiparametric methods kernel estimators human growth analysis

Citation

Kneip, Alois; Engel, Joachim. Model Estimation in Nonlinear Regression Under Shape Invariance. Ann. Statist. 23 (1995), no. 2, 551--570. doi:10.1214/aos/1176324535. https://projecteuclid.org/euclid.aos/1176324535


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