Uncertainty Quantification in Lasso-Type Regularization Problems Article Swipe
Tathagata Basu
,
Jochen Einbeck
,
Matthias C. M. Troffaes
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-030-60166-9_3
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-030-60166-9_3
Regularization techniques, which sit at the interface of statistical modeling and machine learning, are often used in the engineering or other applied sciences to tackle high dimensional regression (type) problems. While a number of regularization methods are commonly used, the 'Least Absolute Shrinkage and Selection Operator' or simply LASSO is popular because of its efficient variable selection property. This property of the LASSO helps to deal with problems where the number of predictors is larger than the total number of observations, as it shrinks the coefficients of non-important parameters to zero. In this chapter, both frequentist and Bayesian approaches for the LASSO are discussed, with particular attention to the problem of uncertainty quantification of regression parameters. For the frequentist approach, we discuss a refit technique as well as the classical bootstrap method, and for the Bayesian method, we make use of the equivalent LASSO formulation using a Laplace prior on the model parameters.
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- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.1007/978-3-030-60166-9_3
- OA Status
- gold
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- 1
- References
- 29
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- OpenAlex ID
- https://openalex.org/W3132407402
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https://openalex.org/W3132407402Canonical identifier for this work in OpenAlex
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https://doi.org/10.1007/978-3-030-60166-9_3Digital Object Identifier
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Uncertainty Quantification in Lasso-Type Regularization ProblemsWork title
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book-chapterOpenAlex work type
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enPrimary language
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2020Year of publication
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2020-09-10Full publication date if available
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Tathagata Basu, Jochen Einbeck, Matthias C. M. TroffaesList of authors in order
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https://doi.org/10.1007/978-3-030-60166-9_3Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://durham-repository.worktribe.com/output/1625807Direct OA link when available
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Frequentist inference, Lasso (programming language), Regularization (linguistics), Elastic net regularization, Bayesian probability, Mathematics, Feature selection, Computer science, Artificial intelligence, Bayesian inference, Applied mathematics, Machine learning, World Wide WebTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.property. | 57 |
| abstract_inverted_index.selection | 56 |
| abstract_inverted_index.technique | 124 |
| abstract_inverted_index.approaches | 98 |
| abstract_inverted_index.discussed, | 103 |
| abstract_inverted_index.equivalent | 142 |
| abstract_inverted_index.parameters | 88 |
| abstract_inverted_index.particular | 105 |
| abstract_inverted_index.predictors | 72 |
| abstract_inverted_index.regression | 27, 114 |
| abstract_inverted_index.dimensional | 26 |
| abstract_inverted_index.engineering | 18 |
| abstract_inverted_index.formulation | 144 |
| abstract_inverted_index.frequentist | 95, 118 |
| abstract_inverted_index.parameters. | 115, 152 |
| abstract_inverted_index.statistical | 8 |
| abstract_inverted_index.techniques, | 1 |
| abstract_inverted_index.uncertainty | 111 |
| abstract_inverted_index.coefficients | 85 |
| abstract_inverted_index.non-important | 87 |
| abstract_inverted_index.observations, | 80 |
| abstract_inverted_index.Regularization | 0 |
| abstract_inverted_index.quantification | 112 |
| abstract_inverted_index.regularization | 34 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.79890561 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |