Uncertainty Quantification Techniques in Statistics

Language: English

Published by MDPI AG, 2020

3039285467 / 9783039285464

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Seller Inventory # 9783039285464

Title
Uncertainty Quantification Techniques in Statistics
Author
Jong-Min Kim
Publisher
MDPI AG
Publication year
2020
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3039285467
ISBN 13
9783039285464
Item weight
342 grams
Dimensions
244x170x9 mm

AHA-BUCH GmbH

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