Scealy, J.L., Hingee, K.L., Kent, J. orcid.org/0000-0002-1861-8349 et al. (1 more author) (2024) Robust score matching for compositional data. Statistics and Computing, 34. 93. ISSN 0960-3174
Abstract
The restricted polynomially-tilted pairwise interaction (RPPI) distribution gives a flexible model for compositional data. It is particularly well-suited to situations where some of the marginal distributions of the components of a composition are concentrated near zero, possibly with right skewness. This article develops a method of tractable robust estimation for the model by combining two ideas. The first idea is to use score matching estimation after an additive log-ratio transformation. The resulting estimator is automatically insensitive to zeros in the data compositions. The second idea is to incorporate suitable weights in the estimating equations. The resulting estimator is additionally resistant to outliers. These properties are confirmed in simulation studies where we further also demonstrate that our new outlier-robust estimator is efficient in high concentration settings, even in the case when there is no model contamination. An example is given using microbiome data. A user-friendly R package accompanies the article.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Zeros, Log-ratios, PPI model, Outliers |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Feb 2024 11:15 |
Last Modified: | 17 Oct 2024 12:46 |
Status: | Published online |
Publisher: | Springer |
Identification Number: | 10.1007/s11222-024-10412-w |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209593 |