Deconvolution density estimation for compositional data
Abstract Compositional data, which capture relative contributions of parts to a whole, are constrained by a constant sum. They arise in various fields such as geochemistry, soil science, and paleoecology, describing data typically affected by measurement errors. We discuss the kernel method for contaminated compositional data, using a deconvolution approach. Some numerical experiments are provided using both simulated and real data.
Authors
- Stefania Fensore (ORCID: https://orcid.org/0000-0002-5308-2586)
- Marco Di Marzio (ORCID: https://orcid.org/0000-0003-3500-1890)
- Agnese Panzera (ORCID: https://orcid.org/0000-0002-1276-3414)
- Chiara Passamonti (ORCID: https://orcid.org/0009-0000-3376-2157)
Institutions
- University of Chieti-Pescara (IT)
- Azienda USL di Pescara (IT)
- University of Florence (IT)
Publication Details
- Journal
- Statistical Methods & Applications
- Published
- 2026-08-19
- DOI
- https://doi.org/10.1007/s10260-026-00880-x
- Primary Topic
- Geochemistry and Geologic Mapping
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Università degli Studi di Firenze