Prediction Intervals for Integrals of Some Types of Non-Gaussian Random Fields: A Semiparametric Bootstrap Approach

• Activity Number: 247
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Victor De Oliveira* and Bazoumana Kone
• Companies: The University of Texas at San Antonio and PPD
• Keywords: Block average ; Geostatistics ; Kriging ; Spatial average

Abstract

This work proposes a method to construct prediction intervals for integrals of non-Gaussian random fields over bounded regions (called block averages in the geostatistical literature). The method uses a semiparametric approach that does not require distributional assumptions, but only parametric assumptions about the mean and covariance functions of the random field. The resulting semiparametric bootstrap prediction interval overcomes some drawbacks of the commonly used plug-in block kriging prediction interval: the former has better coverage probability properties than the later since it accounts for the uncertainty from parameter estimation, and does not rely on the assumption of Gaussianity. The method is illustrated in the prediction of block averages of cadmium traces in a potentially contaminated region in Switzerland.