Skip to main content
padlock icon - secure page this page is secure

Two-step estimation of functional linear models with applications to longitudinal data

Buy Article:

The full text article is temporarily unavailable.

We apologise for the inconvenience. Please try again later.

Functional linear models are useful in longitudinal data analysis. They include many classical and recently proposed statistical models for longitudinal data and other functional data. Recently, smoothing spline and kernel methods have been proposed for estimating their coefficient functions nonparametrically but these methods are either intensive in computation or inefficient in performance. To overcome these drawbacks, in this paper, a simple and powerful two-step alternative is proposed. In particular, the implementation of the proposed approach via local polynomial smoothing is discussed. Methods for estimating standard deviations of estimated coefficient functions are also proposed. Some asymptotic results for the local polynomial estimators are established. Two longitudinal data sets, one of which involves time-dependent covariates, are used to demonstrate the approach proposed. Simulation studies show that our two-step approach improves the kernel method proposed by Hoover and co-workers in several aspects such as accuracy, computational time and visual appeal of the estimators.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Functional analysis of variance; Functional linear models; Local polynomial smoothing; Longitudinal data analysis

Document Type: Research Article

Affiliations: University of North Carolina, Chapel Hill, USA

Publication date: February 1, 2000

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more