A Bayesian coregionalization approach for multivariate pollutant data

By Alexandra M. Schmidt, Alan E. Gelfand. 2003

175 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a3213aa2599ca23e0b32a9237594a01b0070316f

How researchers see this paper

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

In Economics and Econometrics

1. Marc G. Genton
2. Tatiyana V. Apanasovich
3. Mikyoung Jun
4. Martin Schlather
5. Ying Sun
6. Tilmann Gneiting
7. William Kleiber
8. Anandamayee Majumdar

In Environmental Engineering

1. Marc G. Genton
2. Mikyoung Jun
3. Tatiyana V. Apanasovich
4. Martin Schlather
5. Tilmann Gneiting
6. Ying Sun
7. William Kleiber
8. Emilio Porcu

In Statistics and Probability

1. Marc G. Genton
2. Mikyoung Jun
3. Tatiyana V. Apanasovich
4. Tilmann Gneiting
5. Anandamayee Majumdar
6. Yew-Soon Ong
7. Jianfei Cai
8. Haitao Liu

Citations in the corpus

• Non‐stationary Cross‐Covariance Models for Multivariate Processes on a Globe (2011, methodology): “for all L1, L2, l1, l2 (Stein 2007). Stein (2007) and Jun and Stein (2008) show that the total column ozone process is longitudinally irreversible; we find that it is also the case for both temperature and precipitation data marginally (not shown).”
• Remarks on multi-output Gaussian process regression (2018, methodology): “Linear model of coregionalization The most widely used generative approach for developing admissible multioutput covariance functions was pioneered in the geostatistics community, known as linear model of coregionalization (LMC) [36, 37, 38, 39].”
• A Valid Matérn Class of Cross-Covariance Functions for Multivariate Random Fields With Any Number of Components (2012, methodology): “A popular approach is to use a linear model of coregionalization (LMC) for stationary random fields (Goulard and Votz 1992; Schmidt and Gelfand 2003; Wackernagel 2003; Gelfand et al. 2004; Zhang 2007).”
• Cross-covariance functions for multivariate random fields based on latent dimensions (2010, methodology): “…stations, at which we predict the levels of all three pollutants with the conditional means and we compare the predictions with the ones obtained by using the linear model of coregionalization of Schmidt & Gelfand (2003) available only for NO2, and covariance convolution (Majumdar & Gelfand, 2007).”
• Multivariate Spatial Modeling for Geostatistical Data Using Convolved Covariance Functions (2007, methodology): “Following Schmidt and Gelfand (2003), in order to achieve approximate normality, we use the logarithm of the daily average of each of these variables.”
• Matérn Cross-Covariance Functions for Multivariate Random Fields (2010, background): “Bayesian approaches have become popular over the past two decades (Handcock and Stein 1993; Berger, De Oliveira, and Sansó, 2001) and allow for extensions to multivariate settings (Schmidt and Gelfand 2003; Reich and Fuentes 2007).”
• A Spatio-Temporal Downscaler for Output From Numerical Models (2010, methodology): “These are, in turn, modeled as correlated spatial Gaussian processes via the method of coregionalization (Schmidt and Gelfand 2003; Gelfand et al. 2004).”