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.
Deconvolution density estimation for compositional data / Marco Di Marzio, Stefania Fensore, Agnese Panzera, Chiara Passamonti. - In: STATISTICAL METHODS & APPLICATIONS. - ISSN 1613-981X. - STAMPA. - (In corso di stampa), pp. 1-20. [10.1007/s10260-026-00880-x]
Deconvolution density estimation for compositional data
Agnese Panzera
;
In corso di stampa
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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