Uncertainty Quantification Techniques in Statistics

Uncertainty quantification (UQ) is a mainstream research topic in applied mathematics and statistics. To identify UQ problems, diverse modern techniques for large and complex data analyses have been developed in applied mathematics, computer science, and statistics. This Special Issue of Mathematics...

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Bibliographische Detailangaben
1. Verfasser:	Kim, Jong-Min
Format:	Online
Sprache:	Englisch
Veröffentlicht:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Schlagworte:	HM401-1281 H1-99 Kullback–Leibler divergence geometric distribution accuracy AUROC allele read counts mixture model low-coverage entropy gene-expression data SCAD data envelopment analysis LASSO high-throughput sandwich variance estimator adaptive lasso semiparametric regression ?1 lasso Laplacian matrix elastic net feature selection sea surface temperature gene expression data Skew-Reflected-Gompertz distribution lasso next-generation sequencing BH-FDR stochastic frontier model ?2 ridge geometric mean resampling Gompertz distribution adapative lasso group efficiency comparison sensitive attribute MCP probability proportional to size (PPS) sampling randomization device SIS Yennum et al.’s model ensembles bic Book Industry Communication::J Society & social sciences::JF Society & culture: general::JFF Social issues & processes::JFFP Social interaction
Online-Zugang:	45992
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Uncertainty Quantification Techniques in Statistics

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