topological techniques in model selection
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Embargoed until: 5555-01-01
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Embargoed until: 5555-01-01
Reason: Version not permitted.
Volume
13
Pagination
41 - 56
Publisher
Publisher URL
DOI
10.2140/astat.2022.13.41
Journal
Journal of Algebraic Statistics
Issue
Metadata
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This paper is concerned with enhancing the performance of Lasso for squarefree hierarchical polynomial models. We propose a compound crite- rion that combines validation error with a measure of model complexity, and the measure of model complexity is a sum of Betti numbers of the model, seen as a simplicial complex. The compound criteria helps model selection in polynomial regression models containing higher-order interactions. Sim- ulation results and a real data example show that the compound criteria produces sparser models with lower prediction errors than other statistical methods.
Authors
Maruri-Aguilar, H; HU, S; MA, ZCollections
- Mathematics [1681]