2020
DOI: 10.48550/arxiv.2002.05137
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Non-parametric regression models for compositional data

Abstract: Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. This paper, through use of the α-transformation, extends the classical k-N N regression to what is termed α-k-N N regression, yielding a highly flexible non-parametric regression model for compositional data. Unlike many of the recommended regression models for compositional data, zeros values (which commonly occur in practice) are not problematic and they… Show more

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Cited by 3 publications
(2 citation statements)
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References 47 publications
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“…Besides political science, such data are prevalent in many other fields such as sedimentology [66,1], hydrochemistry [100], economics [95], and bioinformatics [144,26,118]. Regression methods have been developed with compositional data as covariates [118,87,136], as response [1,72,66,56,140,141], or as both covariates and response [28]. But there are few variable selection methods proposed for data with compositional response and Euclidean covariates, as in our case here.…”
Section: Tablementioning
confidence: 99%
“…Besides political science, such data are prevalent in many other fields such as sedimentology [66,1], hydrochemistry [100], economics [95], and bioinformatics [144,26,118]. Regression methods have been developed with compositional data as covariates [118,87,136], as response [1,72,66,56,140,141], or as both covariates and response [28]. But there are few variable selection methods proposed for data with compositional response and Euclidean covariates, as in our case here.…”
Section: Tablementioning
confidence: 99%
“…Other proposals use the Dirichlet distribution to model the compositional responses and link the Dirichlet parameters to covariates (see, e.g., Gueorguieva et al, 2008;Hijazi, 2003;Hijazi & Jernigan, 2009;Van der Merwe, 2019). These models can be easily extended to allow for non-parametric functional forms in the relationship between the model parameters and the predictors (see, e.g., Di Marzio et al, 2015;Tsagris et al, 2020). However, they rely on particular parametric distributional forms which limits the type of inferences that can be obtained.…”
Section: Introductionmentioning
confidence: 99%