Bernoulli

  • Bernoulli
  • Volume 17, Number 4 (2011), 1400-1419.

Estimation for an additive growth curve model with orthogonal design matrices

Jianhua Hu, Guohua Yan, and Jinhong You

Full-text: Open access

Abstract

An additive growth curve model with orthogonal design matrices is proposed in which observations may have different profile forms. The proposed model allows us to fit data and then estimate parameters in a more parsimonious way than the traditional growth curve model. Two-stage generalized least-squares estimators for the regression coefficients are derived where a quadratic estimator for the covariance of observations is taken as the first-stage estimator. Consistency, asymptotic normality and asymptotic independence of these estimators are investigated. Simulation studies and a numerical example are given to illustrate the efficiency and parsimony of the proposed model for model specifications in the sense of minimizing Akaike’s information criterion (AIC).

Article information

Source
Bernoulli, Volume 17, Number 4 (2011), 1400-1419.

Dates
First available in Project Euclid: 4 November 2011

Permanent link to this document
https://projecteuclid.org/euclid.bj/1320417510

Digital Object Identifier
doi:10.3150/10-BEJ315

Mathematical Reviews number (MathSciNet)
MR2854778

Zentralblatt MATH identifier
1229.62073

Keywords
AIC asymptotic normality consistent estimator growth curve model quadratic estimator two-stage generalized least squares

Citation

Hu, Jianhua; Yan, Guohua; You, Jinhong. Estimation for an additive growth curve model with orthogonal design matrices. Bernoulli 17 (2011), no. 4, 1400--1419. doi:10.3150/10-BEJ315. https://projecteuclid.org/euclid.bj/1320417510


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