Uncertainty Quantification Techniques in Statistics

• Main Author: Kim, Jong-Min (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: 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 Access: 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 (ISSN 2227-7390) includes diverse modern data analysis methods such as skew-reflected-Gompertz information quantifiers with application to sea surface temperature records, the performance of variable selection and classification via a rank-based classifier, two-stage classification with SIS using a new filter ranking method in high throughput data, an estimation of sensitive attribute applying geometric distribution under probability proportional to size sampling, combination of ensembles of regularized regression models with resampling-based lasso feature selection in high dimensional data, robust linear trend test for low-coverage next-generation sequence data controlling for covariates, and comparing groups of decision-making units in efficiency based on semiparametric regression.