Enhanced Variable Selection for Boosting Sparser and Less Complex Models in Distributional Copula Regression Article Swipe
Annika Strömer
,
Nadja Klein
,
Christian Staerk
,
Florian Faschingbauer
,
Hannah Klinkhammer
,
Andreas Mayr
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s12561-025-09491-8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s12561-025-09491-8
Structured additive distributional copula regression allows to model the joint distribution of multivariate outcomes by relating all distribution parameters to covariates. Estimation via statistical boosting enables accounting for high-dimensional data and incorporating data-driven variable selection, both of which are useful given the complexity of the model class. However, as known from univariate (distributional) regression, the standard boosting algorithm tends to select too many variables with minor importance, particularly in settings with large sample sizes, leading to complex models with difficult interpretation. To counteract this behavior and to avoid selecting base-learners with only a negligible impact, we combine the ideas of probing, stability selection, and a new deselection approach with statistical boosting for distributional copula regression. In simulations and an application to the joint modeling of weight and length of newborns, we find that all proposed methods enhance variable selection by reducing the number of false positives. However, only stability selection and the deselection approach yield similar predictive performance to classical boosting. Finally, the deselection approach is better scalable to larger datasets and leads to competitive predictive performance, which we further illustrate in a genomic cohort study from the UK Biobank by modeling the joint genetic predisposition for two phenotypes.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s12561-025-09491-8
- https://link.springer.com/content/pdf/10.1007/s12561-025-09491-8.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411372172
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https://openalex.org/W4411372172Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s12561-025-09491-8Digital Object Identifier
- Title
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Enhanced Variable Selection for Boosting Sparser and Less Complex Models in Distributional Copula RegressionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-17Full publication date if available
- Authors
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Annika Strömer, Nadja Klein, Christian Staerk, Florian Faschingbauer, Hannah Klinkhammer, Andreas MayrList of authors in order
- Landing page
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https://doi.org/10.1007/s12561-025-09491-8Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s12561-025-09491-8.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s12561-025-09491-8.pdfDirect OA link when available
- Concepts
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Copula (linguistics), Feature selection, Biostatistics, Econometrics, Boosting (machine learning), Regression, Statistics, Model selection, Computer science, Mathematics, Artificial intelligence, Public health, Nursing, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.larger | 170 |
| abstract_inverted_index.length | 128 |
| abstract_inverted_index.models | 78 |
| abstract_inverted_index.number | 143 |
| abstract_inverted_index.sample | 73 |
| abstract_inverted_index.select | 61 |
| abstract_inverted_index.sizes, | 74 |
| abstract_inverted_index.useful | 40 |
| abstract_inverted_index.weight | 126 |
| abstract_inverted_index.Biobank | 190 |
| abstract_inverted_index.combine | 97 |
| abstract_inverted_index.complex | 77 |
| abstract_inverted_index.enables | 26 |
| abstract_inverted_index.enhance | 137 |
| abstract_inverted_index.further | 180 |
| abstract_inverted_index.genetic | 195 |
| abstract_inverted_index.genomic | 184 |
| abstract_inverted_index.impact, | 95 |
| abstract_inverted_index.leading | 75 |
| abstract_inverted_index.methods | 136 |
| abstract_inverted_index.similar | 156 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Finally, | 162 |
| abstract_inverted_index.However, | 48, 147 |
| abstract_inverted_index.additive | 2 |
| abstract_inverted_index.approach | 108, 154, 165 |
| abstract_inverted_index.behavior | 85 |
| abstract_inverted_index.boosting | 25, 57, 111 |
| abstract_inverted_index.datasets | 171 |
| abstract_inverted_index.modeling | 124, 192 |
| abstract_inverted_index.outcomes | 14 |
| abstract_inverted_index.probing, | 101 |
| abstract_inverted_index.proposed | 135 |
| abstract_inverted_index.reducing | 141 |
| abstract_inverted_index.relating | 16 |
| abstract_inverted_index.scalable | 168 |
| abstract_inverted_index.settings | 70 |
| abstract_inverted_index.standard | 56 |
| abstract_inverted_index.variable | 34, 138 |
| abstract_inverted_index.algorithm | 58 |
| abstract_inverted_index.boosting. | 161 |
| abstract_inverted_index.classical | 160 |
| abstract_inverted_index.difficult | 80 |
| abstract_inverted_index.newborns, | 130 |
| abstract_inverted_index.selecting | 89 |
| abstract_inverted_index.selection | 139, 150 |
| abstract_inverted_index.stability | 102, 149 |
| abstract_inverted_index.variables | 64 |
| abstract_inverted_index.Estimation | 22 |
| abstract_inverted_index.Structured | 1 |
| abstract_inverted_index.accounting | 27 |
| abstract_inverted_index.complexity | 43 |
| abstract_inverted_index.counteract | 83 |
| abstract_inverted_index.illustrate | 181 |
| abstract_inverted_index.negligible | 94 |
| abstract_inverted_index.parameters | 19 |
| abstract_inverted_index.positives. | 146 |
| abstract_inverted_index.predictive | 157, 176 |
| abstract_inverted_index.regression | 5 |
| abstract_inverted_index.selection, | 35, 103 |
| abstract_inverted_index.univariate | 52 |
| abstract_inverted_index.application | 120 |
| abstract_inverted_index.competitive | 175 |
| abstract_inverted_index.covariates. | 21 |
| abstract_inverted_index.data-driven | 33 |
| abstract_inverted_index.deselection | 107, 153, 164 |
| abstract_inverted_index.importance, | 67 |
| abstract_inverted_index.performance | 158 |
| abstract_inverted_index.phenotypes. | 199 |
| abstract_inverted_index.regression, | 54 |
| abstract_inverted_index.regression. | 115 |
| abstract_inverted_index.simulations | 117 |
| abstract_inverted_index.statistical | 24, 110 |
| abstract_inverted_index.distribution | 11, 18 |
| abstract_inverted_index.multivariate | 13 |
| abstract_inverted_index.particularly | 68 |
| abstract_inverted_index.performance, | 177 |
| abstract_inverted_index.base-learners | 90 |
| abstract_inverted_index.incorporating | 32 |
| abstract_inverted_index.distributional | 3, 113 |
| abstract_inverted_index.predisposition | 196 |
| abstract_inverted_index.interpretation. | 81 |
| abstract_inverted_index.(distributional) | 53 |
| abstract_inverted_index.high-dimensional | 29 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.92585204 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |