Risser, Mark D.

1. Review: Nonstationary Spatial Modeling, with Emphasis on Process\n Convolution and Covariate-Driven Approaches
   2016/10/07 by Mark D. Risser, Risser, Mark D. · 6 citations
   Chemistry · Computer Science · Environmental Science · Mathematics · #FOS: Computer and information sciences #Geochemistry and Geologic Mapping #Methodology (stat.ME) #Soil Geostatistics and Mapping #Spectroscopy and Chemometric Analyses #Statistical Methods and Inference
2. Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R
   2015/07/30 by Risser, Mark D., Calder, Catherine A. · 2 citations
   #Computation (stat.CO) #FOS: Computer and information sciences
3. Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
   2022/05/18 by Marcus M. Noack, Harinarayan Krishnan, Noack, Marcus M. +5 · 2 citations
   Computer Science · #Advanced Multi-Objective Optimization Algorithms #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Probability (math.PR)
4. Granger causal inference for climate change attribution
   2024/08/13 by Mark D. Risser, Risser, Mark D., Mohammed Ombadi +3 · 3 citations
   Environmental Science · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #Qualitative Comparative Analysis Research #Sustainability and Climate Change Governance
5. A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
   2023/09/18 by Marcus M. Noack, Hengrui Luo, Noack, Marcus M. +3 · 2 citations
   Computer Science · Engineering · Environmental Science · #Air Quality Monitoring and Forecasting #FOS: Computer and information sciences #FOS: Mathematics #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probability (math.PR)
6. Spatial scale-aware tail dependence modeling for high-dimensional spatial extremes
   2024/12/10 by Shi, Muyang, Zhang, Likun, Risser, Mark D. +1 · 2 citations
   #FOS: Computer and information sciences #Methodology (stat.ME)
7. Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
   2024/11/07 by Mark D. Risser, Marcus M. Noack, Risser, Mark D. +5 · 1 citation
   Computer Science · #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Neural Networks and Applications