JaLCDOI 10.18926/fest/19714
FullText URL 004_037_055.pdf
Author Choi, Seung Bae| Tanaka, Yutaka|
Abstract One of major problems in spatial analysis is to estimate the value z(s(0)) at an unknown location s(0) using the information about observations z(s(α)), α = 1,…,n. In this article, we will perform a numerical study about some methods for this problem. That is, we examine both the tranditional statistical method which does not take into account spatial correlation and the spatial statistical method which takes into account spatial correlation by applying them to a set of non-stationary spatial data. We compare the predictive powers of these methods. More precisely, we choose Universal Kriging(UK) and Median-Polish Kriging(MPK) as spatial statistical methods, and locally weighted regression or LOESS as a traditional method. As the major criterion for comparison, we use the so-called PRESS statistic, and also draw the prediction surface plot and the prediction standard error surface plot as minor criteria. A real numerical example of non-stantionary spatial data is analyzed for the comparison among the above three methods.
Keywords Stationary Variogram UK MPK LOESS
Publication Title 岡山大学環境理工学部研究報告
Published Date 1999-02-26
Volume volume4
Issue issue1
Start Page 37
End Page 55
ISSN 1341-9099
language 英語
File Version publisher
NAID 120002309080
JaLCDOI 10.18926/fest/11561
FullText URL 005_035_046.pdf
Author Choi, Seung Bae| Tanaka, Yutaka|
Abstract Spatial data is analyzed in three stages of 1) estimating the variograms, 2) fitting a model for the estimated variograms and 3) predicting the value at unknown location based on the information at known locations (kriging). Recently, it has become a subject of interest to detect influential observations in these stages. Choi and Tanaka(1999) have derived influence functions in the above three stages and have proposed sensitivity analysis procedure. So far influence functions have only been derived for variograms by Gunst and Hartfield(1996). The present article makes a comparison of the performances between those influence functions for variograms derived by Choi and Tanaka(1999) and by Gunst and Hartfield(1996). A real numerical example is given to discuss the validity or usefulness of those influence functions.
Keywords Stationary spatal data Influence function Sample variogram Median-polish residual
Publication Title 岡山大学環境理工学部研究報告
Published Date 2000-02-29
Volume volume5
Issue issue1
Start Page 35
End Page 46
ISSN 1341-9099
language 英語
File Version publisher
NAID 120002313332