EconPapers    
Economics at your fingertips  
 

Minimax rate in prediction for functional principal component regression

Guangren Yang, Hongmei Lin and Heng Lian

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 5, 1240-1249

Abstract: In this short paper, we consider the convergence rate of functional linear regression in prediction loss. For upper bound we consider estimation of the slope function based on functional principal component analysis. Lower bound is also obtained that shows the convergence rate obtained using functional principal component regression is optimal.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2019.1649429 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:50:y:2021:i:5:p:1240-1249

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2019.1649429

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:lstaxx:v:50:y:2021:i:5:p:1240-1249