Optimal sampling designs for nonparametric estimation of spatial averages of random fields

Karim Benhenni and Yingcai Su

Journal of Multivariate Analysis, 2016, vol. 146, issue C, 341-351

Abstract: Optimal designs of sampling spatial locations in estimating spatial averages of random fields are considered. The random field is assumed to have correlated values according to a covariance function. The quality of estimation is measured by the mean squared error. Simple nonparametric linear estimators along with sampling designs having a limiting density are considered. For a large class of locally isotropic random fields, we argue for the asymptotic optimality of simple linear estimators. The convergent rates of the mean squared error and optimal limiting densities of sampling designs are determined in every dimension. An example of simulation is given.

Keywords: Nonparametric estimation; Random field; Sampling design; Spatial average (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X15003139
Full text for ScienceDirect subscribers only

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:eee:jmvana:v:146:y:2016:i:c:p:341-351

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2015.11.010

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().