Optimal spatial prediction using ensemble machine learning

DOI10.1515/ijb-2014-0060MaRDI QIDQ6632730

Molly Margaret Davies, Mark Johannes van der Laan

Publication date: 5 November 2024

Published in: The International Journal of Biostatistics (Search for Journal in Brave)

zbMATH Keywords

cross-validationspatial interpolationoracle inequalitysuper learnergeneralized stacking

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

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• Asymptotics of cross-validated risk estimation in estimator selection and performance assess\-ment
• Model-based geostatistics.
• A survey of cross-validation procedures for model selection
• Spatial variation. 2nd ed
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Polynomial splines and their tensor products in extended linear modeling. (With discussions)
• Nonparametric regression with correlated errors.
• A distribution-free theory of nonparametric regression
• Stacked regressions
• Statistical Methods for Spatial Data Analysis
• Geostatistical Model Averaging for Locally Calibrated Probabilistic Quantitative Precipitation Forecasting
• Oracle inequalities for multi-fold cross validation
• Handbook of Spatial Statistics
• Optimal Geostatistical Model Selection
• The Predictive Sample Reuse Method with Applications
• Combining Estiamates in Regression and Classification
• Smoothing parameter selection methods for nonparametric regression with spatially correlated errors
• Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics
• Random forests
• Smoothing spline ANOVA models

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