Uncertainty Quantification Techniques in Statistics

• Hoofdauteur: Kim, Jong-Min (auth)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Onderwerpen: 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
• Online toegang: DOAB: download the publication; DOAB: description of the publication

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...