EconPapers    
Economics at your fingertips  
 

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

Jianqing Fan and J.‐T. Zhang

Journal of the Royal Statistical Society Series B, 2000, vol. 62, issue 2, 303-322

Abstract: 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.

Date: 2000
References: Add references at CitEc
Citations: View citations in EconPapers (61) Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1111/1467-9868.00233

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:62:y:2000:i:2:p:303-322

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868

Access Statistics for this article

Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom

More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2019-09-27
Handle: RePEc:bla:jorssb:v:62:y:2000:i:2:p:303-322