Central limit theorem for the kernel estimator of the regression function for censored time series

Zohra Guessoum and Elias Ould Saïd

Journal of Nonparametric Statistics, 2012, vol. 24, issue 2, 379-397

Abstract: In this paper, we consider the estimation of the regression function when the interest variable is subject to random censorship and the data satisfy some dependency conditions. We show that the new estimate [defined in Guessoum, Z., and Ould Saïd, E. (2008),‘On Nonparametric Estimation of the Regression Function Under Censorship Model’, Statistics & Decisions, 26, 159–177] suitably normalised is asymptotically normally distributed and the asymptotic variance is given explicitly. An application to confidence bands is given. Some simulations are drawn to lend further support to our theoretical results and to compare finite samples sizes with different rates of censoring and dependence.

Date: 2012
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2011.640678 (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:24:y:2012:i:2:p:379-397

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

DOI: 10.1080/10485252.2011.640678

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 ().