Economics at your fingertips  

Self-normalization for Spatial Data

Xianyang Zhang, Bo Li and Xiaofeng Shao ()

Scandinavian Journal of Statistics, 2014, vol. 41, issue 2, 311-324

Abstract: type="main" xml:id="sjos12028-abs-0001"> This paper considers inference for both spatial lattice data with possibly irregularly shaped sampling region and non-lattice data, by extending the recently proposed self-normalization (SN) approach from stationary time series to the spatial setup. A nice feature of the SN method is that it avoids the choice of tuning parameters, which are usually required for other non-parametric inference approaches. The extension is non-trivial as spatial data has no natural one-directional time ordering. The SN-based inference is convenient to implement and is shown through simulation studies to provide more accurate coverage compared with the widely used subsampling approach. We also illustrate the idea of SN using a real data example.

Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) (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:

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0303-6898

Access Statistics for this article

Scandinavian Journal of Statistics is currently edited by ÿrnulf Borgan and Bo Lindqvist

More articles in Scandinavian Journal of Statistics from Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association, Swedish Statistical Association
Bibliographic data for series maintained by Wiley Content Delivery ().

Page updated 2019-07-26
Handle: RePEc:bla:scjsta:v:41:y:2014:i:2:p:311-324