Surface estimation under local stationarity
Sucharita Ghosh
Journal of Nonparametric Statistics, 2015, vol. 27, issue 2, 229-240
Abstract:
Consider a nonparametric regression model involving spatial observations that are nonlinear transformations of a latent Gaussian random field. We address estimation of the variance of the Priestley-Chao kernel estimator of the surface by using a local stationarity-type property which is a result of the assumed transformation. It turns out that it is possible to avoid estimation of the various nuisance parameters so as to estimate the leading term of the asymptotic variance of the estimator. We also address uniform convergence of the nonparametric surface estimator, under short-memory and long-memory correlations in the data.
Date: 2015
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2015.1029473 (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:27:y:2015:i:2:p:229-240
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20
DOI: 10.1080/10485252.2015.1029473
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 ().