Proposes PcovRnnp method enabling simultaneous dimension reduction and regularized coefficient estimation via nuclear norm penalty in high-dimensional settings.
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A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
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Principal Covariate Regression with Nuclear Norm Penalty
Proposes PcovRnnp method enabling simultaneous dimension reduction and regularized coefficient estimation via nuclear norm penalty in high-dimensional settings.