Low-rank matrix factorization with autoregressive regularization, periodic detrending, and cross-spectral covariance imputes missing SSI measurements in two stages and supplies distribution-free uncertainty intervals via conformal prediction.
Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation
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abstract
We derive a single pass algorithm for computing the gradient and Fisher information of Vecchia's Gaussian process loglikelihood approximation, which provides a computationally efficient means for applying the Fisher scoring algorithm for maximizing the loglikelihood. The advantages of the optimization techniques are demonstrated in numerical examples and in an application to Argo ocean temperature data. The new methods are more accurate and much faster than an optimization method that uses only function evaluations, especially when the covariance function has many parameters. This allows practitioners to fit nonstationary models to large spatial and spatial-temporal datasets.
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Matrix Factorization-Based Solar Spectral Irradiance Missing Data Imputation with Uncertainty Quantification
Low-rank matrix factorization with autoregressive regularization, periodic detrending, and cross-spectral covariance imputes missing SSI measurements in two stages and supplies distribution-free uncertainty intervals via conformal prediction.