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Uncertainty Quantification Techniques in Statistics
Jong-Min Kim
2020
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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.
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Keywords
- ?1 lasso
- ?2 ridge
- accuracy
- adapative lasso
- adaptive lasso
- allele read counts
- AUROC
- BH-FDR
- Data envelopment analysis
- elastic net
- ensembles
- Entropy
- feature selection
- gene expression data
- gene-expression data
- geometric distribution
- geometric mean
- gompertz distribution
- group efficiency comparison
- high-throughput
- Kullback–Leibler divergence
- Laplacian matrix
- LASSO
- low-coverage
- MCP
- mixture model
- next-generation sequencing
- probability proportional to size (PPS) sampling
- randomization device
- resampling
- sandwich variance estimator
- SCAD
- Sea Surface Temperature
- semiparametric regression
- sensitive attribute
- SIS
- Skew-Reflected-Gompertz distribution
- stochastic frontier model
- Yennum et al.’s model
Links
DOI: 10.3390/books978-3-03928-547-1Editions
