Geostatistical Interpolation of Positively Skewed and Censored Data in a Dioxin-Contaminated Site

By Hirotaka Saito, Pierre Goovaerts. 2000

132 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c2d514524f1051ebe580c0f3a4cc8280ccb23e07

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In Artificial Intelligence

In Environmental Engineering

In Statistics and Probability

Citations in the corpus

Comparative soil CO2 flux measurements and geostatistical estimation methods on Masaya volcano, Nicaragua (2004, methodology): “Prediction performances of OK may be better when the sample histogram is normally distributed [e.g., Saito and Goovaerts , 2000].”
On unbiased backtransform of lognormal kriging estimates (2007, background): “In this sense, several authors have reported this problem, such as: David ([2], p. 119), Chiles and Delfiner ([1], p. 192), and Saito and Goovaerts ([ 9 ], p. 4230), among others.”
Geostatistical interpolation of object counts collected from multiple strip transects: Ordinary kriging versus finite domain kriging (2005, background): “Upton and Fingleton (1985), Buckland et al. (2001), and others discuss the differences between line and strip transects.”
A World Malaria Map: Plasmodium falciparum Endemicity in 2007 (2009, methodology): “The ability to predict known values of PfPR2 10 was summarised using mean error as a measure of overall bias, mean absolute error as a measure of overall accuracy, and the correlation coefficient as a measure of linear association [44,66].”
Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest. (2005, methodology): “…to characterize canopy structure (Cohen et al., 1990; Hudak & Wessman, 1998; St-Onge & Cavayas, 1997; Wulder et al., 1998), to estimate continuous (Hudak et al., 2002) and categorical variables (Milne & Cohen, 1999; Rossi et al., 1993), and to assess risk (Myers, 1997; Saito & Goovaerts, 2000).”
Copula-based geostatistical modeling of continuous and discrete data including covariates (2010, methodology): “Hence, we adapt a method proposed by Saito and Goovaerts (2000) who used it in the case of a normal-score transformation.”