Optimal Geostatistical Model Selection

By Hsin‐Cheng Huang, Chun-Shu Chen. 2007

61 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Environmental Chemistry, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/ad66f6b8d709f65a4ed41f86e0b3e0875d59cf91

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jorge Mateu
2. Carlo Gaetan
3. Moreno Bevilacqua
4. Emilio Porcu
5. Tilmann Gneiting

In Environmental Chemistry

1. Jorge Mateu
2. Carlo Gaetan
3. Moreno Bevilacqua
4. Emilio Porcu

In Environmental Engineering

1. Haonan Wang
2. Jun Zhu
3. Jorge Mateu
4. Carlo Gaetan
5. Moreno Bevilacqua
6. Emilio Porcu
7. Tingjin Chu
8. Xiaofeng Shao

Citations in the corpus

• Penalized maximum likelihood estimation and variable selection in geostatistics (2011, background): “Huang and Chen [11] developed a model selection criterion in geostatistics, but for the purpose of Kriging rather than selection of covariates.”
• Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach (2012, methodology): “, Huang and Chen 2007). Hoeting et al. (2006) proposed a model selection criteria based on AIC and BIC in the spatial and space-time setting. Their proposals rely on the evaluation of the likelihood for a Gaussian random field. Unfortunately, in the case of massive datasets, the selection criteria b”