Uncertainty Quantification Techniques in Statistics

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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Bibliographic Details
Main Author: Kim, Jong-Min (auth)
Format: Book Chapter
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
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