A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.
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Theta-regularized Kriging penalizes the theta hyperparameter in Gaussian stochastic processes using Lasso, Ridge, or Elastic-net, yielding higher accuracy and stability than prior penalized Kriging variants on numerical tests and engineering cases.
Recursive multi-fidelity GP regression with EM optimization trains faster than the coupled non-recursive Kennedy-O'Hagan approach on noisy non-nested data while delivering comparable predictions and uncertainty estimates.
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Linking COPD Prevalence with Income Distribution: A Spatial Heterogeneous Compositional Regression via Geographically Weighted Penalized Approach
A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.
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Theta-regularized Kriging: Modelling and Algorithms
Theta-regularized Kriging penalizes the theta hyperparameter in Gaussian stochastic processes using Lasso, Ridge, or Elastic-net, yielding higher accuracy and stability than prior penalized Kriging variants on numerical tests and engineering cases.
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Multi-fidelity Gaussian process regression for noisy outputs and non-nested experimental designs: a comparison between the recursive and non-recursive formulations
Recursive multi-fidelity GP regression with EM optimization trains faster than the coupled non-recursive Kennedy-O'Hagan approach on noisy non-nested data while delivering comparable predictions and uncertainty estimates.