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Spatial best linear unbiased prediction: A computational mathematics approach for high dimensional massive datasets
April 25, 2024, 7:45 p.m. | Julio E. Castrillon-Candas
stat.ML updates on arXiv.org arxiv.org
Abstract: With the advent of massive data sets much of the computational science and engineering community has moved toward data-intensive approaches in regression and classification. However, these present significant challenges due to increasing size, complexity and dimensionality of the problems. In particular, covariance matrices in many cases are numerically unstable and linear algebra shows that often such matrices cannot be inverted accurately on a finite precision computer. A common ad hoc approach to stabilizing a matrix …
abstract arxiv challenges classification community complexity computational data data sets datasets dimensionality engineering however linear massive mathematics prediction regression science spatial stat.co stat.ml type unbiased
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