Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis

Mathematics

• Particulate Matter 100%
• Spatio-temporal Data 88%
• Large Data 71%
• Gaussian Model 65%
• Nonseparable 64%
• Process Model 62%
• Nearest Neighbor 58%
• Gaussian Process 55%
• Spatio-temporal Process 22%
• Air Quality 11%
• Bayesian Hierarchical Model 10%
• Sparse Approximation 10%
• Pollutants 9%
• Low-rank Approximation 9%
• Scalability 9%
• High Resolution 8%
• Large Data Sets 8%
• Covariance Structure 7%
• Chemistry 7%
• Sparsity 7%
• Health 7%
• Quantify 7%
• Random Effects 7%
• Numerical Study 6%
• Human 6%
• Space-time 6%
• Uncertainty 5%
• Requirements 5%
• Prediction 5%
• Class 4%
• Approximation 3%
• Model 2%

Business & Economics

• Particulate Matter 85%
• Gaussian Process 79%
• Process Model 77%
• Nearest Neighbor 75%
• Approximation 17%
• Bayesian Hierarchical Model 11%
• Air Quality 8%
• Human Health 8%
• Scalability 8%
• Random Effects 7%
• Inference 5%
• Prediction 5%
• Uncertainty 4%

Engineering & Materials Science

• Specification languages 12%
• Air quality 12%
• Large dataset 12%
• Scalability 9%
• Health 8%
• Uncertainty 7%
• Data storage equipment 6%