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@book{aitchison1986,
title = {The {{Statistical Analysis}} of {{Compositional Data}}},
author = {Aitchison, J.},
date = {1986},
series = {Monographs on Statistics and Applied Probability},
publisher = {{Chapman and Hall}},
location = {{Londres, UK ; New York, USA}},
doi = {10.1007/978-94-009-4109-0},
isbn = {978-94-009-4109-0 978-94-010-8324-9},
langid = {english},
pagetotal = {416}
}

@article{aitchison2002,
title = {Biplots of Compositional Data},
author = {Aitchison, John and Greenacre, Michael},
date = {2002-10},
date = {2002},
journaltitle = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
shortjournal = {J Royal Statistical Soc C},
volume = {51},
Expand All @@ -25,205 +12,47 @@ @article{aitchison2002
langid = {english}
}

@article{baxter2008,
title = {On {{Statistical Approaches}} to the {{Study}} of {{Ceramic Artefacts Using Geochemical}} and {{Petrographic Data}}},
author = {Baxter, M. J. and Beardah, C. C. and Papageorgiou, I. and Cau, M. A. and Day, P. M. and Kilikoglou, V.},
date = {2008-02},
journaltitle = {Archaeometry},
volume = {50},
number = {1},
pages = {142--157},
issn = {0003813X},
doi = {10.1111/j.1475-4754.2007.00359.x},
abstract = {The scientific analysis of ceramics often has the aim of identifying groups of similar artefacts. Much published work focuses on analysis of data derived from geochemical or mineralogical techniques. The former is more likely to be subjected to quantitative statistical analysis. This paper examines some approaches to the statistical analysis of data arising from both kinds of techniques, including ‘mixed-mode’ methods where both types of data are incorporated into analysis. The approaches are illustrated using data derived from 88 Late Bronze Age transport jars from Kommos, Crete. Results suggest that the mixed-mode approach can provide additional insight into the data.},
langid = {english}
}

@inproceedings{beardah2003,
title = {"{{Mixed-mode}}" Approaches to the Grouping of Ceramic Artefacts Using {{S-Plus}}},
booktitle = {The {{Digital Heritage}} of {{Archaeology}}.},
author = {Beardah, C. C. and Baxter, M. J. and Papageorgiou, I. and Cau, M. A.},
editor = {Doerr, M. and Sarris, A.},
date = {2003},
pages = {261--266},
publisher = {{Archive of Monuments and Publications, Hellenic Ministry of Culture}},
location = {{Athens}},
eventtitle = {{{CAA2002}} ({{Heraklion}}, {{Crete}}; {{April}} 2002)},
langid = {english}
}

@article{cau2004,
title = {Exploring Automatic Grouping Procedures in Ceramic Petrology},
author = {Cau, Miguel-Angel and Day, Peter M and Baxter, Michael J and Papageorgiou, Ioulia and Iliopoulos, Ioannis and Montana, Giuseppe},
date = {2004-09},
journaltitle = {Journal of Archaeological Science},
volume = {31},
number = {9},
pages = {1325--1338},
issn = {03054403},
doi = {10.1016/j.jas.2004.03.006},
langid = {english}
}

@article{egozcue2003,
title = {Isometric {{Logratio Transformations}} for {{Compositional Data Analysis}}},
author = {Egozcue, J. J. and Pawlowsky-Glahn, V. and Mateu-Figueras, G. and Barceló-Vidal, C.},
date = {2003-04},
journaltitle = {Mathematical Geology},
volume = {35},
number = {3},
pages = {279--300},
doi = {10.1023/A:1023818214614},
abstract = {Geometry in the simplex has been developed in the last 15 years mainly based on the contributions due to J. Aitchison. The main goal was to develop analytical tools for the statistical analysis of compositional data. Our present aim is to get a further insight into some aspects of this geometry in order to clarify the way for more complex statistical approaches. This is done by way of orthonormal bases, which allow for a straightforward handling of geometric elements in the simplex. The transformation into real coordinates preserves all metric properties and is thus called isometric logratio transformation (ilr). An important result is the decomposition of the simplex, as a vector space, into orthogonal subspaces associated with nonoverlapping subcompositions. This gives the key to join compositions with different parts into a single composition by using a balancing element. The relationship between ilr transformations and the centered-logratio (clr) and additive-logratio (alr) transformations is also studied. Exponential growth or decay of mass is used to illustrate compositional linear processes, parallelism and orthogonality in the simplex.},
langid = {english}
}

@article{filzmoser2005,
title = {Multivariate Outlier Detection in Exploration Geochemistry},
author = {Filzmoser, Peter and Garrett, Robert G. and Reimann, Clemens},
date = {2005-06},
journaltitle = {Computers \& Geosciences},
volume = {31},
number = {5},
pages = {579--587},
issn = {00983004},
doi = {10.1016/j.cageo.2004.11.013},
langid = {english}
}

@article{filzmoser2008,
title = {Outlier {{Detection}} for {{Compositional Data Using Robust Methods}}},
author = {Filzmoser, Peter and Hron, Karel},
date = {2008-04},
journaltitle = {Mathematical Geosciences},
volume = {40},
number = {3},
pages = {233--248},
issn = {1874-8961, 1874-8953},
doi = {10.1007/s11004-007-9141-5},
abstract = {Outlier detection based on the Mahalanobis distance (MD) requires an appropriate transformation in case of compositional data. For the family of logratio transformations (additive, centered and isometric logratio transformation) it is shown that the MDs based on classical estimates are invariant to these transformations, and that the MDs based on affine equivariant estimators of location and covariance are the same for additive and isometric logratio transformation. Moreover, for 3-dimensional compositions the data structure can be visualized by contour lines. In higher dimension the MDs of closed and opened data give an impression of the multivariate data behavior.},
langid = {english}
}

@article{filzmoser2009,
title = {Principal Component Analysis for Compositional Data with Outliers},
author = {Filzmoser, Peter and Hron, Karel and Reimann, Clemens},
date = {2009-09},
journaltitle = {Environmetrics},
volume = {20},
number = {6},
pages = {621--632},
issn = {11804009, 1099095X},
doi = {10.1002/env.966},
abstract = {Compositional data (almost all data in geochemistry) are closed data, that is they usually sum up to a constant (e.g. weight percent, wt.\%) and carry only relative information. Thus, the covariance structure of compositional data is strongly biased and results of many multivariate techniques become doubtful without a proper transformation of the data. The centred logratio transformation (clr) is often used to open closed data. However the transformed data do not have full rank following a logratio transformation and cannot be used for robust multivariate techniques like principal component analysis (PCA). Here we propose to use the isometric logratio transformation (ilr) instead. However, the ilr transformation has the disadvantage that the resulting new variables are no longer directly interpretable in terms of the originally entered variables. Here we propose a technique how the resulting scores and loadings of a robust PCA on ilr transformed data can be back-transformed and interpreted. The procedure is demonstrated using a real data set from regional geochemistry and compared to results from non-transformed and non-robust versions of PCA. It turns out that the procedure using ilr-transformed data and robust PCA delivers superior results to all other approaches. The examples demonstrate that due to the compositional nature of geochemical data PCA should not be carried out without an appropriate transformation. Furthermore a robust approach is preferable if the dataset contains outliers.},
langid = {english}
}

@article{filzmoser2012,
title = {Interpretation of Multivariate Outliers for Compositional Data},
author = {Filzmoser, Peter and Hron, Karel and Reimann, Clemens},
date = {2012-02},
journaltitle = {Computers \& Geosciences},
volume = {39},
pages = {77--85},
issn = {00983004},
doi = {10.1016/j.cageo.2011.06.014},
abstract = {Compositional data—and most data in geochemistry are of this type—carry relative rather than absolute information. For multivariate outlier detection methods this implies that not the given data but appropriately transformed data need to be used. We use the isometric logratio (ilr) transformation, which seems to be generally the most proper one for theoretical and practical reasons. In this space it is difficult to interpret the outliers, because the reason for outlyingness can be complex. Therefore we introduce tools that support the interpretation of outliers by representing multivariate information in biplots, maps, and univariate scatterplots.},
langid = {english}
}

@book{filzmoser2018,
title = {Applied {{Compositional Data Analysis}}: {{With Worked Examples}} in {{R}}},
shorttitle = {Applied {{Compositional Data Analysis}}},
author = {Filzmoser, Peter and Hron, Karel and Templ, Matthias},
date = {2018},
series = {Use {{R}}!},
publisher = {{Springer-Verlag}},
location = {{Berlin Heidelberg}},
doi = {10.1007/978-3-319-96422-5},
isbn = {978-3-319-96420-1 978-3-319-96422-5},
langid = {english}
}

@article{fiserova2011,
title = {On the {{Interpretation}} of {{Orthonormal Coordinates}} for {{Compositional Data}}},
author = {Fišerová, Eva and Hron, Karel},
date = {2011-05},
journaltitle = {Mathematical Geosciences},
volume = {43},
number = {4},
pages = {455--468},
issn = {1874-8961, 1874-8953},
doi = {10.1007/s11004-011-9333-x},
langid = {english}
}

@book{greenacre2019,
title = {Compositional Data Analysis in Practice},
@book{greenacre1984,
title = {Theory and Applications of Correspondence Analysis},
author = {Greenacre, Michael J.},
date = {2019},
series = {Chapman \& {{Hall}}/{{CRC}} Interdisciplinary Statistics},
publisher = {{CRC Press, Taylor \& Francis Group}},
location = {{Boca Raton}},
isbn = {978-1-138-31661-4 978-1-138-31643-0},
pagetotal = {121}
}

@article{greenacre2021,
title = {Compositional {{Data Analysis}}},
author = {Greenacre, Michael},
date = {2021-03-07},
journaltitle = {Annual Review of Statistics and Its Application},
shortjournal = {Annu. Rev. Stat. Appl.},
volume = {8},
number = {1},
pages = {271--299},
issn = {2326-8298, 2326-831X},
doi = {10.1146/annurev-statistics-042720-124436},
abstract = {Compositional data are nonnegative data carrying relative, rather than absolute, information—these are often data with a constant-sum constraint on the sample values, for example, proportions or percentages summing to 1\% or 100\%, respectively. Ratios between components of a composition are important since they are unaffected by the particular set of components chosen. Logarithms of ratios (logratios) are the fundamental transformation in the ratio approach to compositional data analysis—all data thus need to be strictly positive, so that zero values present a major problem. Components that group together based on domain knowledge can be amalgamated (i.e., summed) to create new components, and this can alleviate the problem of data zeros. Once compositional data are transformed to logratios, regular univariate and multivariate statistical analysis can be performed, such as dimension reduction and clustering, as well as modeling. Alternative methodologies that come close to the ideals of the logratio approach are also considered, especially those that avoid the problem of data zeros, which is particularly acute in large bioinformatic data sets.},
date = {1984},
publisher = {Academic Press},
location = {London ; Orlando, Fla},
langid = {english}
}

@article{hron2010,
title = {Imputation of Missing Values for Compositional Data Using Classical and Robust Methods},
author = {Hron, K. and Templ, M. and Filzmoser, P.},
date = {2010-12},
journaltitle = {Computational Statistics \& Data Analysis},
shortjournal = {Computational Statistics \& Data Analysis},
volume = {54},
number = {12},
pages = {3095--3107},
issn = {01679473},
doi = {10.1016/j.csda.2009.11.023},
langid = {english}
@book{greenacre2007,
title = {Correspondence Analysis in Practice},
author = {Greenacre, Michael J.},
date = {2007},
series = {Interdisciplinary Statistics Series},
edition = {seconde edition},
publisher = {Chapman \& Hall/CRC},
location = {Boca Raton},
isbn = {978-1-58488-616-7},
langid = {english},
pagetotal = {280}
}

@article{hron2011,
title = {Statistical {{Properties}} of the {{Total Variation Estimator}} for {{Compositional Data}}},
author = {Hron, Karel and Kubáček, Lubomír},
date = {2011-09},
journaltitle = {Metrika},
volume = {74},
number = {2},
pages = {221--230},
issn = {0026-1335, 1435-926X},
doi = {10.1007/s00184-010-0299-3},
abstract = {The sample space of compositional data, a simplex, induces a different kind of geometry, known as Aitchison geometry, with the Euclidean space property. For this reason, the standard statistical analysis is not meaningful here, and this is also true for measures of location and covariance. The measure of location, called centre, is the best linear unbiased estimator of the central tendency of the distribution of a random composition with respect to the geometry on the simplex (Pawlowsky-Glahn and Egozcue in Stoch Envir Res Risk Ass, 15:384–398, 2001; Math Geol, 34:259–274, 2002). Its covariance structure is described through a variation matrix, which induces the so called total variation as a measure of dispersion. The aim of the paper is to show that its sample counterpart has theoretical properties, corresponding to the standard multivariate case, like unbiasedness and convergence in probability. Moreover, its distribution in the case of normality on the simplex is developed.},
langid = {english}
@book{greenacre2010,
title = {Biplots in Practice},
author = {Greenacre, Michael J.},
date = {2010},
publisher = {Fundación BBVA},
location = {Bilbao},
isbn = {978-84-923846-8-6},
langid = {english},
pagetotal = {237}
}

@article{hron2017,
title = {Weighted {{Pivot Coordinates}} for {{Compositional Data}} and {{Their Application}} to {{Geochemical Mapping}}},
author = {Hron, Karel and Filzmoser, Peter and family=Caritat, given=Patrice, prefix=de, useprefix=true and Fišerová, Eva and Gardlo, Alžběta},
date = {2017-08},
journaltitle = {Mathematical Geosciences},
shortjournal = {Math Geosci},
volume = {49},
number = {6},
pages = {797--814},
issn = {1874-8961, 1874-8953},
doi = {10.1007/s11004-017-9684-z},
langid = {english}
@book{lebart2006,
title = {Statistique exploratoire multidimensionnelle : Visualisations et inférences en fouilles de données},
shorttitle = {Statistique exploratoire multidimensionnelle},
author = {Lebart, Ludovic and Piron, Marie and Morineau, Alain},
date = {2006},
abstract = {Cette quatrième édition est entièrement refondue. Appuyé sur de nombreux exemples, l'ouvrage présente les concepts de base et les fondements des méthodes exploratoires et rend compte des développements récents. Il insiste sur la place centrale, dans la démarche «Fouille de Données», des visualisations fondées sur des principes géométriques et algébriques simples, sous le contrôle de méthodes inférentielles robustes.},
isbn = {978-2-10-049616-7},
langid = {french}
}

@article{lockyear2013,
Expand All @@ -240,33 +69,6 @@ @article{lockyear2013
langid = {english}
}

@article{martin-fernandez2003,
title = {Dealing with {{Zeros}} and {{Missing Values}} in {{Compositional Data Sets Using Nonparametric Imputation}}},
author = {Martín-Fernández, J. A. and Barceló-Vidal, C. and Pawlowsky-Glahn, V.},
date = {2003},
journaltitle = {Mathematical Geology},
volume = {35},
number = {3},
pages = {253--278},
issn = {08828121},
doi = {10.1023/A:1023866030544},
langid = {english}
}

@article{pawlowsky-glahn2001,
title = {Geometric Approach to Statistical Analysis on the Simplex},
author = {Pawlowsky-Glahn, V. and Egozcue, J. J.},
date = {2001-10},
journaltitle = {Stochastic Environmental Research and Risk Assessment},
volume = {15},
number = {5},
pages = {384--398},
issn = {14363240},
doi = {10.1007/s004770100077},
abstract = {The geometric interpretation of the expected value and the variance in real Euclidean space is used as a starting point to introduce metric counterparts on an arbitrary finite dimensional Hilbert space. This approach allows us to define general reasonable properties for estimators of parameters, like metric unbiasedness and minimum metric variance, resulting in a useful tool to better understand the logratio approach to the statistical analysis of compositional data, who's natural sample space is the simplex.},
langid = {english}
}

@article{ringrose1992,
title = {Bootstrapping and Correspondence Analysis in Archaeology},
author = {Ringrose, T.J.},
Expand All @@ -281,56 +83,19 @@ @article{ringrose1992
langid = {english}
}

@article{rousseeuw1990,
title = {Unmasking {{Multivariate Outliers}} and {{Leverage Points}}},
author = {Rousseeuw, Peter J. and family=Zomeren, given=Bert C., prefix=van, useprefix=true},
date = {1990-09},
journaltitle = {Journal of the American Statistical Association},
shortjournal = {Journal of the American Statistical Association},
volume = {85},
number = {411},
pages = {633--639},
issn = {0162-1459, 1537-274X},
doi = {10.1080/01621459.1990.10474920},
langid = {english}
}

@article{santos2020,
title = {Modern Methods for Old Data: {{An}} Overview of Some Robust Methods for Outliers Detection with Applications in Osteology},
shorttitle = {Modern Methods for Old Data},
author = {Santos, Frédéric},
date = {2020-08},
journaltitle = {Journal of Archaeological Science: Reports},
shortjournal = {Journal of Archaeological Science: Reports},
volume = {32},
pages = {102423},
issn = {2352409X},
doi = {10.1016/j.jasrep.2020.102423},
langid = {english}
}

@book{vandenboogaart2013,
title = {Analyzing {{Compositional Data}} with {{R}}},
author = {family=Boogaart, given=K. Gerald, prefix=van den, useprefix=true and Tolosana-Delgado, Raimon},
date = {2013},
series = {Use {{R}}!},
publisher = {{Springer-Verlag}},
location = {{Berlin Heidelberg}},
doi = {10.1007/978-3-642-36809-7},
isbn = {978-3-642-36808-0},
@article{wiklund2008,
title = {Visualization of {{GC}}/{{TOF-MS-Based Metabolomics Data}} for {{Identification}} of {{Biochemically Interesting Compounds Using OPLS Class Models}}},
author = {Wiklund, Susanne and Johansson, Erik and Sjöström, Lina and Mellerowicz, Ewa J. and Edlund, Ulf and Shockcor, John P. and Gottfries, Johan and Moritz, Thomas and Trygg, Johan},
date = {2008-01-01},
journaltitle = {Analytical Chemistry},
shortjournal = {Anal. Chem.},
volume = {80},
number = {1},
pages = {115--122},
issn = {0003-2700, 1520-6882},
doi = {10.1021/ac0713510},
url = {https://pubs.acs.org/doi/10.1021/ac0713510},
urldate = {2023-05-23},
langid = {english},
pagetotal = {285}
}

@incollection{weigand1977,
title = {Turquoise {{Sources}} and {{Source Analysisis}}: {{Mesoamerica}} and the {{Southwestern U}}.{{S}}.{{A}}.},
shorttitle = {Turquoise {{Sources}} and {{Source Analysisis}}},
booktitle = {Exchange {{Systems}} in {{Prehistory}}},
author = {Weigand, P. C. and Harbottle, G. and Sayre, E.},
editor = {Ericson, J. and Earle, T. K.},
date = {1977},
pages = {15--34},
publisher = {{Academic Press}},
location = {{New York, NY}},
langid = {english}
keywords = {TODO}
}

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