Asymptotic results in gamma kernel regression
Jianhong Shi and
Weixing Song
Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 12, 3489-3509
Abstract:
Based on the Gamma kernel density estimation procedure, this article constructs a nonparametric kernel estimate for the regression functions when the covariate are nonnegative. Asymptotic normality and uniform almost sure convergence results for the new estimator are systematically studied, and the finite performance of the proposed estimate is discussed via a simulation study and a comparison study with an existing method. Finally, the proposed estimation procedure is applied to the Geyser data set.
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2014.890225 (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:45:y:2016:i:12:p:3489-3509
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2014.890225
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 ().