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...

Full description

Saved in:
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
Online Access:Get Fullteks
DOAB: description of the publication
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Physical Description:1 electronic resource (128 p.)
ISBN:books978-3-03928-547-1
9783039285464
9783039285471
Access:Open Access

Similar Items