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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| Үндсэн зохиолч: | |
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| Формат: | Online |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Нөхцлүүд: | |
| Онлайн хандалт: | 45992 |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| _version_ | 1869525985146175488 |
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| author | Kim, Jong-Min |
| author_browse | Kim, Jong-Min |
| author_facet | Kim, Jong-Min |
| author_sort | Kim, Jong-Min |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-61517 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-615172023-12-20T15:54:28Z Uncertainty Quantification Techniques in Statistics Kim, Jong-Min HM401-1281 H1-99 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 bic Book Industry Communication::J Society & social sciences::JF Society & culture: general::JFF Social issues & processes::JFFP Social interaction 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. 2021-02-12T06:53:04Z 2021-02-12T06:53:04Z 2020-06-09 16:38:57 2020 book 45992 9783039285464 9783039285471 https://directory.doabooks.org/handle/20.500.12854/61517 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/2166 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03928-547-1 10.3390/books978-3-03928-547-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039285464 9783039285471 128 open access |
| spellingShingle | HM401-1281 H1-99 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 bic Book Industry Communication::J Society & social sciences::JF Society & culture: general::JFF Social issues & processes::JFFP Social interaction Kim, Jong-Min Uncertainty Quantification Techniques in Statistics |
| title | Uncertainty Quantification Techniques in Statistics |
| title_full | Uncertainty Quantification Techniques in Statistics |
| title_fullStr | Uncertainty Quantification Techniques in Statistics |
| title_full_unstemmed | Uncertainty Quantification Techniques in Statistics |
| title_short | Uncertainty Quantification Techniques in Statistics |
| title_sort | uncertainty quantification techniques in statistics |
| topic | HM401-1281 H1-99 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 bic Book Industry Communication::J Society & social sciences::JF Society & culture: general::JFF Social issues & processes::JFFP Social interaction |
| topic_facet | HM401-1281 H1-99 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 bic Book Industry Communication::J Society & social sciences::JF Society & culture: general::JFF Social issues & processes::JFFP Social interaction |
| url | 45992 |
| work_keys_str_mv | AT kimjongmin uncertaintyquantificationtechniquesinstatistics |