Robust estimation for partially linear single-index model

Zean Li, Weihua Zhao, Riquan Zhang and Jicai Liu

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 11, 5342-5356

Abstract: In this article, we investigate a new estimation approach for the partially linear single-index model based on modal regression method, where the non parametric function is estimated by penalized spline method. Moreover, we develop an expection maximum (EM)-type algorithm and establish the large sample properties of the proposed estimation method. A distinguishing characteristic of the newly proposed estimation is robust against outliers through introducing an additional tuning parameter which can be automatically selected using the observed data. Simulation studies and real data example are used to evaluate the finite-sample performance, and the results show that the newly proposed method works very well.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2015.1100739 (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:46:y:2017:i:11:p:5342-5356

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

DOI: 10.1080/03610926.2015.1100739

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 ().