zCompositions — R package for multivariate imputation of left-censored data under a compositional approach

Palarea‐Albaladejo, Javier; Fernández, Josep Antoni Martín

doi:10.1016/j.chemolab.2015.02.019

Cited by 674 publications

(446 citation statements)

References 47 publications

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“…This is fundamentally a different problem than that addressed by recent work on rounded zeros, or below-detection level zeros, such as in Palarea-Albaladejo and Martín-Fernández (2015) and references therein. Although there are recent workable Bayesian approaches to zeros in compositions from count data, Martín-Fernández, Hron, Templ, Filzmoser, and Palarea-Albaladejo (2014) and references therein, essential zeros in continuous compositions still present a problem.…”

Section: Introductionmentioning

confidence: 52%

A Logistic Normal Mixture Model for Compositional Data Allowing Essential Zeros

Bear

Billheimer

2016

AJS

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The usual candidate distributions for modeling compositions, the Dirichlet and the logistic normal distribution, do not include zero components in their support. Methods have been developed and refined for dealing with zeros that are rounded, or due to a value being below a detection level. Methods have also been developed for zeros in compositions arising from count data. However, essential zeros, cases where a component is truly absent, in continuous compositions are still a problem.The most promising approach is based on extending the logistic normal distribution to model essential zeros using a mixture of additive logistic normal distributions of different dimension, related by common parameters. We continue this approach, and by imposing an additional constraint, develop a likelihood, and show ways of estimating parameters for location and dispersion. The proposed likelihood, conditional on parameters for the probability of zeros, is a mixture of additive logistic normal distributions of different dimensions whose location and dispersion parameters are projections of a common location or dispersion parameter. For some simple special cases, we contrast the relative efficiency of different location estimators.

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Section: Introductionmentioning

confidence: 52%

A Logistic Normal Mixture Model for Compositional Data Allowing Essential Zeros

Bear

Billheimer

2016

AJS

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“…For more details on single versus multiple imputation, see the discussion Imputation Parameters below. Because imputation is a multivariate procedure that considers not only the distribution of individual tracers but also the relationships between the tracers (Little and Rubin, 1987;Lubin and others, 2004), it can only be applied to a dataset, not a univariate vector (Palarea-Albaladejo and Martin-Fernandez, 2015). In Sed_SAT, the source dataset can be imputed for each source type.…”

Section: Imputationmentioning

confidence: 99%

Determining the sources of fine-grained sediment using the Sediment Source Assessment Tool (Sed_SAT)

Sanisaca¹,

Gellis²,

Lorenz³

2017

Open-File Report

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“…to Palarea-Albaladejo and Martín-Fernández (2015) those elements can be replaced with specific techniques. Palarea-Albaladejo and Martín-Fernández (2013) give a review of these techniques.…”

Section: Control Chartmentioning

confidence: 99%

Signal interpretation in Hotelling’sT²control chart for compositional data

2016

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Nowadays, control of concentrations of elements is of crucial importance in industry. Concentrations are expressed in terms of proportions or percentages which means that they are compositional data (CoDa). CoDa are defined as vectors of positive elements that represent parts of a whole and usually add to a constant sum. Classical T 2 control chart is not appropriate for CoDa, for which is better to use a compositional T 2 control chart (T 2 C CC). This paper generalizes the interpretation of the out-of-control signals of the individual T 2 C CC for more than three components. We propose two methods for identifying the ratio of components that mainly contribute to the signal. The first one is suitable for low dimensional problems and consists on finding the log ratio of components that maximizes the univariate T 2 statistic. The second one is an optimized method for large dimensional problems that simplifies the calculus by transforming the coordinates into the sphere. We illustrate the T 2 C CC signal interpretation with a practical example from the chemical and pharmaceutical industry.

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zCompositions — R package for multivariate imputation of left-censored data under a compositional approach

Cited by 674 publications

References 47 publications

A Logistic Normal Mixture Model for Compositional Data Allowing Essential Zeros

A Logistic Normal Mixture Model for Compositional Data Allowing Essential Zeros

Determining the sources of fine-grained sediment using the Sediment Source Assessment Tool (Sed_SAT)

Signal interpretation in Hotelling’sT²control chart for compositional data

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zCompositions — R package for multivariate imputation of left-censored data under a compositional approach

Cited by 674 publications

References 47 publications

A Logistic Normal Mixture Model for Compositional Data Allowing Essential Zeros

A Logistic Normal Mixture Model for Compositional Data Allowing Essential Zeros

Determining the sources of fine-grained sediment using the Sediment Source Assessment Tool (Sed_SAT)

Signal interpretation in Hotelling’sT2control chart for compositional data

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Signal interpretation in Hotelling’sT²control chart for compositional data