Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-27459Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kook, Lucas | - |
| dc.contributor.author | Sick, Beate | - |
| dc.contributor.author | Bühlmann, Peter | - |
| dc.date.accessioned | 2023-03-27T15:28:47Z | - |
| dc.date.available | 2023-03-27T15:28:47Z | - |
| dc.date.issued | 2022 | - |
| dc.identifier.issn | 0960-3174 | de_CH |
| dc.identifier.issn | 1573-1375 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/27459 | - |
| dc.description.abstract | Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable but difficult-to-achieve property for prediction models and in general requires strong assumptions on the data generating process (DGP). In a causally inspired perspective on OOD generalization, the test data arise from a specific class of interventions on exogenous random variables of the DGP, called anchors. Anchor regression models, introduced by Rothenhäusler et al. (J R Stat Soc Ser B 83(2):215-246, 2021. 10.1111/rssb.12398), protect against distributional shifts in the test data by employing causal regularization. However, so far anchor regression has only been used with a squared-error loss which is inapplicable to common responses such as censored continuous or ordinal data. Here, we propose a distributional version of anchor regression which generalizes the method to potentially censored responses with at least an ordered sample space. To this end, we combine a flexible class of parametric transformation models for distributional regression with an appropriate causal regularizer under a more general notion of residuals. In an exemplary application and several simulation scenarios we demonstrate the extent to which OOD generalization is possible. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Springer | de_CH |
| dc.relation.ispartof | Statistics and Computing | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject | Anchor regression | de_CH |
| dc.subject | Covariate shift | de_CH |
| dc.subject | Diluted causality | de_CH |
| dc.subject | Distributional regression | de_CH |
| dc.subject | Out-of-distribution generalization | de_CH |
| dc.subject | Transformation models | de_CH |
| dc.subject.ddc | 510: Mathematik | de_CH |
| dc.title | Distributional anchor regression | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Datenanalyse und Prozessdesign (IDP) | de_CH |
| dc.identifier.doi | 10.1007/s11222-022-10097-z | de_CH |
| dc.identifier.doi | 10.21256/zhaw-27459 | - |
| dc.identifier.pmid | 35582000 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.issue | 3 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.start | 39 | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 32 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_Kook-Sick-Buehlmann_Distributional-anchor-regression_Statisticsandcomputing.pdf | 2.25 MB | Adobe PDF |  View/Open |
Show simple item record
Kook, L., Sick, B., & Bühlmann, P. (2022). Distributional anchor regression. Statistics and Computing, 32(3), 39. https://doi.org/10.1007/s11222-022-10097-z
Kook, L., Sick, B. and Bühlmann, P. (2022) ‘Distributional anchor regression’, Statistics and Computing, 32(3), p. 39. Available at: https://doi.org/10.1007/s11222-022-10097-z.
L. Kook, B. Sick, and P. Bühlmann, “Distributional anchor regression,” Statistics and Computing, vol. 32, no. 3, p. 39, 2022, doi: 10.1007/s11222-022-10097-z.
KOOK, Lucas, Beate SICK und Peter BÜHLMANN, 2022. Distributional anchor regression. Statistics and Computing. 2022. Bd. 32, Nr. 3, S. 39. DOI 10.1007/s11222-022-10097-z
Kook, Lucas, Beate Sick, and Peter Bühlmann. 2022. “Distributional Anchor Regression.” Statistics and Computing 32 (3): 39. https://doi.org/10.1007/s11222-022-10097-z.
Kook, Lucas, et al. “Distributional Anchor Regression.” Statistics and Computing, vol. 32, no. 3, 2022, p. 39, https://doi.org/10.1007/s11222-022-10097-z.
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