Work

Deconvolution density estimation for compositional data

Year: 2026

Type: article

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.

Source: Statistical Methods & Applications

DOI: 10.1007/s10260-026-00880-x

Authors: Marco Di Marzio, Stefania Fensore, Agnese Panzera, Chiara Passamonti

Institutions: University of Chieti-Pescara, University of Florence

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,290

Funder: Università degli Studi di Firenze