Density and hazard rate estimation for censored and α-mixing data using gamma kernels
Taoufik Bouezmarni and
Jeroen Rombouts
Journal of Nonparametric Statistics, 2008, vol. 20, issue 7, 627-643
Abstract:
In this paper, we consider the non-parametric estimation for a density and hazard rate function for right censored α-mixing survival time data using kernel smoothing techniques. As survival times are positive with potentially high concentration at zero, one has to take into account the bias problems when the functions are estimated in the boundary region. In this paper, gamma kernel estimators of the density and the hazard rate function are proposed. The estimators use adaptive weights depending on the point in which we estimate the function, and they are robust to the boundary bias problem. For both estimators, the mean-squared error properties, including the rate of convergence, the almost sure consistency, and the asymptotic normality, are investigated. The results of a simulation study demonstrate the performance of the proposed estimators.
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://hdl.handle.net/10.1080/10485250802290670 (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:20:y:2008:i:7:p:627-643
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20
DOI: 10.1080/10485250802290670
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 ().