Regularised rank quasi-likelihood estimation for generalised additive models
Hannah E. Correia and
Asheber Abebe
Journal of Nonparametric Statistics, 2021, vol. 33, issue 1, 101-117
Abstract:
Generalised additive models (GAMs) provide flexible models for a wide array of data sources. In the past, improvements of GAM estimation have focused on the smoothers used in the local scoring algorithm used for estimation, but poor prediction for non-Gaussian data motivates the need for robust estimation of GAMs. In this paper, rank-based estimation, as a robust and efficient alternative to the likelihood-based estimation of GAMs, is proposed. It is shown that rank GAM estimators can be obtained through iteratively reweighted likelihood-based GAM estimation which we call the iterated regularised rank quasi-likelihood (IRRQL). Simulation experiments support the use of rank-based GAM estimation for heavy-tailed or contaminated sources of data.
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2021.1921176 (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:33:y:2021:i:1:p:101-117
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20
DOI: 10.1080/10485252.2021.1921176
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 ().