Profile likelihood ratio tests for parameter inferences in generalised single-index models

Nanxi Zhang and Alan Huang

Journal of Nonparametric Statistics, 2018, vol. 30, issue 4, 957-972

Abstract: A profile likelihood ratio test is proposed for inferences on the index coefficients in generalised single-index models. Key features include its simplicity in implementation, invariance against parametrization, and exhibiting substantially less bias than standard Wald-tests in finite-sample settings. Moreover, the R routine to carry out the profile likelihood ratio test is demonstrated to be over two orders of magnitude faster than the recently proposed generalised likelihood ratio test based on kernel regression. The advantages of the method are demonstrated on various simulations and a data analysis example.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2018.1506121 (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:gnstxx:v:30:y:2018:i:4:p:957-972

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2018.1506121

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().