Deconvolution density estimation for compositional data

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The kernel method using a deconvolution approach can be effectively applied to estimate the density of contaminated compositional data affected by measurement errors. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Marco Di Marzio; Stefania Fensore; Agnese Panzera; Chiara Passamonti

doi:10.1007/s10260-026-00880-x

August 19, 2026Statistical Methods & ApplicationsOpen Access

Deconvolution density estimation for compositional data

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Key Points

Develop and evaluate a non-parametric kernel deconvolution density estimation framework specifically adapted for compositional data contaminated with measurement error.
Adapted kernel deconvolution methods to account for simplex sample spaces and constant-sum constraints inherent to compositional data.
Tested and validated the estimation framework across simulated numerical experiments and real-world empirical datasets.
Demonstrated that deconvolution kernel density estimation successfully corrects for measurement error bias in simplex-constrained compositional distributions.
Showed practical utility of the proposed estimator for noisy compositional datasets typical of geochemical, soil, and paleoecological applications.

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Implication

Methodological study demonstrates a deconvolution kernel density estimation framework for error-prone compositional data, highlighting improved distribution recovery in constrained settings.

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.

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Marzio et al. (2026) studied this question.

synapsesocial.com/papers/6a895e96ca7ade938187cad5 https://doi.org/10.1007/s10260-026-00880-x

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Deconvolution density estimation for compositional data

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Deconvolution density estimation for compositional data

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