000190992 001__ 190992
000190992 005__ 20180913062157.0
000190992 0247_ $$2doi$$a10.1080/10618600.2012.729982
000190992 022__ $$a1061-8600
000190992 02470 $$2ISI$$a000326315500005
000190992 037__ $$aARTICLE
000190992 245__ $$aNonstationary Positive Definite Tapering On The Plane
000190992 269__ $$a2013
000190992 260__ $$aAlexandria$$bAmerican Statistical Association$$c2013
000190992 300__ $$a18
000190992 336__ $$aJournal Articles
000190992 520__ $$aA common problem in spatial statistics is to predict a random field f at some spatial location t(0) using observations f(t(1)),..., f(t(n)) at t(1),..., t(n) epsilon IRd. Recent work by Kaufman et al. and Furrer et al. studies the use of tapering for reducing the computational burden associated with likelihood-based estimation and prediction in large spatial datasets. Unfortunately, highly irregular observation locations can present problems for stationary tapers. In particular, there can exist local neighborhoods with too few observations for sufficient accuracy, while others have too many for computational tractability. In this article, we show how to generate nonstationaty covariance tapers T(s, t) such that the number of observations in {t : T(s, t) > 0} is approximately a constant function of s. This ensures that tapering neighborhoods do not have too many points to cause computational problems but simultaneously have enough local points for accurate prediction. We focus specifically on tapering in two dimensions where quasi-conformal theory can be used. Supplementary materials for the article are available online.
000190992 6531_ $$aCovariance tapering
000190992 6531_ $$aKriging
000190992 6531_ $$aOptimization
000190992 6531_ $$aRandom fields.
000190992 700__ $$aAnderes, Ethan$$uUniv Calif Davis, Dept Stat, Davis, CA 95616 USA
000190992 700__ $$0243110$$aHuser, Raphaël$$g166379
000190992 700__ $$aNychka, Douglas$$uNatl Ctr Atmospher Res, CISLs Inst Math Appl Geosci IMAGe, Boulder, CO 80305 USA
000190992 700__ $$aCoram, Marc
000190992 773__ $$j22$$k4$$q848-865$$tJournal Of Computational And Graphical Statistics
000190992 8564_ $$s1508999$$uhttps://infoscience.epfl.ch/record/190992/files/Untitled.pdf$$yPublisher's version$$zPublisher's version
000190992 909C0 $$0252136$$pSTAT$$xU10124
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000190992 917Z8 $$x111184
000190992 937__ $$aEPFL-ARTICLE-190992
000190992 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000190992 980__ $$aARTICLE