A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics

Efron (1979) introduced the bootstrap method for independent data but it cannot be easily applied to spatial data because of their dependency. For spatial data that are correlated in terms of their locations in the underlying space the moving block bootstrap method is usually used to estimate the precision measures of the estimators. The precision of the moving block bootstrap estimators is related to the block size which is difficult to select. In the moving block bootstrap method also the variance estimator is underestimated. In this paper, first the semi-parametric bootstrap is used to estimate the precision measures of estimators in spatial data analysis. In the semi-parametric bootstrap method, we use the estimation of the spatial correlation structure. Then, we compare the semi-parametric bootstrap with a moving block bootstrap for variance estimation of estimators in a simulation study. Finally, we use the semi-parametric bootstrap to analyze the coal-ash data

A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics

Efron (1979) introduced the bootstrap method for independent data but it cannot be easily applied to spatial data because of their dependency. For spatial data that are correlated in terms of their locations in the underlying space the moving block bootstrap method is usually used to estimate the precision measures of the estimators. The precision of the moving block bootstrap estimators is related to the block size which is difficult to select. In the moving block bootstrap method also the variance estimator is underestimated. In this paper, first the semi-parametric bootstrap is used to estimate the precision measures of estimators in spatial data analysis. In the semi-parametric bootstrap method, we use the estimation of the spatial correlation structure. Then, we compare the semi-parametric bootstrap with a moving block bootstrap for variance estimation of estimators in a simulation study. Finally, we use the semi-parametric bootstrap to analyze the coal-ash data.Moving block bootstrap Semi-parametric bootstrap Plug-in kriging Monte Carlo simulation Coal-ash data

Spatial Semi-Parametric Bootstrap Method for Analysis of Kriging Predictor of Random Field

AbstractIn the spatial statistics it is often assumed that the data follow a Gaussian random field. Efron introduced bootstrap method for independent data analysis (IIDB) but it can not be applied in spatial data analysis because of dependency of observations. In this paper, an algorithm is given for spatial semi-parametric bootstrap (SSPB) method to estimate the precision measures of plug-in kriging predictor of random field. We also compare IIDB and SSPB methods for analysis of plug-in kriging predictor in a Monte-Carlo simulation study. Finally, we use SSPB method for analysis of finite strain data in geology. © 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [name organizer

Spatial Analysis Of Three Agrichemicals In Groundwater Of Isfahan Using GS+

The purpose of this study was to undertake a spatial analysis of total organic carbon, electrical conductivity and nitrate, in order to produce a pollution dispersion and prediction map for the investigated area in the province of Isfahan in Iran. The groundwater samples were collected from a zone as a pilot study area of 80 km2, including 25 water wells, based on the criteria of vulnerability assessment projects, that is, about one well per 3 km2, during four seasons in 2008-09. In order to make any inferences about the areas that did not have well data, a statistical relationship between explanatory total organic carbon, electrical conductivity and nitrate variables related to well coordination was developed. The probability of the presence of elevated levels of the three compounds in the groundwater was predicted using the best-fit variogram model. According to spatial analysis, the highest R2=0.789 achieved was related to electrical conductivity and followed the exponential model with 0.266 for NO3- (spherical model) and 0.322 for total organic carbon (exponential model) in the spring 2009. This showed the high confidence level for electrical conductivity dataset and forecasted trends. The results of the spatial analysis demonstrated that the transfer trends of electrical conductivity in the groundwater resources followed the route of groundwater movement in all seasons. However, for nitrate and total organic carbon, a definite trend was not obtained and pollution dispersion depended on many parameters