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Conference Paper: Compound compositional data processes
Title  Compound compositional data processes 

Authors  
Keywords  compound Compositions Mixing process Closure Compositional data Detection limits 
Issue Date  2015 
Citation  The 6th International Workshop on Compositional Data analysis (CoDaWork 2015), L'Escala, Girona, Spain, 15 June 2015. How to Cite? 
Abstract  Compositional data is nonnegative data subject to the unit sum constraint. The logistic normal distribution provides a framework for compositional data when it satisfies subcompositional coherence in that the inference from a sub composition should be the same based on the full composition or the subcomposition alone. However, in many cases subcompositions are not coherent because of additional structure on the compositions, which can be modelled as process(es) inducing change. Sometimes data are collected with a model already well validated and hence with the focus on estimation of the model parameters. Alternatively, sometimes the appropriate model is unknown in advance and it is necessary to use the data to identify a suitable model. In both cases, a hierarchy of possible structure(s) is very helpful. This is evident in the evaluation of, for example, geochemical and household expenditure data. In the case of geochemical data, the structural process might be the stoichiometric constraints induced by the crystal lattice sites, which ensures that amalgamations of some elements are constant in molar terms. The choice of units (weight percent oxide or moles) has an impact on how the data can be modelled and interpreted. For simple igneous systems (e.g. Hawaiian basalt) mineral modes can be calculated from which a valid geochemical interpretation can be obtained. For household expenditure data, the structural process might be how teetotal households have distinct spending patterns on discretionary items from nonteetotal households. Measurement error is an example of another underlying process that reflects how an underlying discrete distribution (e.g. for the number of molecules in a sample) is converted using a linear calibration into a nonnegative measurement, where measurements below the stated detection limit are reported as zero. Compositional perturbation involves additive errors on the logratio space and is the process that does show subcompositional coherence. The mixing process involves the combination of compositions into a new composition, such as minerals combining to form a rock, where there may be considerable knowledge about the set of possible mixing processes. Finally, recording error may affect the composition, such as recording the components to a specified number of decimal digits, implying interval censoring, which implies error is close to uniform on the simplex. 
Persistent Identifier  http://hdl.handle.net/10722/218460 
DC Field  Value  Language 

dc.contributor.author  BaconShone, J   
dc.contributor.author  Grunsky, E   
dc.date.accessioned  20150918T06:38:12Z   
dc.date.available  20150918T06:38:12Z   
dc.date.issued  2015   
dc.identifier.citation  The 6th International Workshop on Compositional Data analysis (CoDaWork 2015), L'Escala, Girona, Spain, 15 June 2015.   
dc.identifier.uri  http://hdl.handle.net/10722/218460   
dc.description.abstract  Compositional data is nonnegative data subject to the unit sum constraint. The logistic normal distribution provides a framework for compositional data when it satisfies subcompositional coherence in that the inference from a sub composition should be the same based on the full composition or the subcomposition alone. However, in many cases subcompositions are not coherent because of additional structure on the compositions, which can be modelled as process(es) inducing change. Sometimes data are collected with a model already well validated and hence with the focus on estimation of the model parameters. Alternatively, sometimes the appropriate model is unknown in advance and it is necessary to use the data to identify a suitable model. In both cases, a hierarchy of possible structure(s) is very helpful. This is evident in the evaluation of, for example, geochemical and household expenditure data. In the case of geochemical data, the structural process might be the stoichiometric constraints induced by the crystal lattice sites, which ensures that amalgamations of some elements are constant in molar terms. The choice of units (weight percent oxide or moles) has an impact on how the data can be modelled and interpreted. For simple igneous systems (e.g. Hawaiian basalt) mineral modes can be calculated from which a valid geochemical interpretation can be obtained. For household expenditure data, the structural process might be how teetotal households have distinct spending patterns on discretionary items from nonteetotal households. Measurement error is an example of another underlying process that reflects how an underlying discrete distribution (e.g. for the number of molecules in a sample) is converted using a linear calibration into a nonnegative measurement, where measurements below the stated detection limit are reported as zero. Compositional perturbation involves additive errors on the logratio space and is the process that does show subcompositional coherence. The mixing process involves the combination of compositions into a new composition, such as minerals combining to form a rock, where there may be considerable knowledge about the set of possible mixing processes. Finally, recording error may affect the composition, such as recording the components to a specified number of decimal digits, implying interval censoring, which implies error is close to uniform on the simplex.   
dc.language  eng   
dc.relation.ispartof  International Workshop on Compositional Data analysis, CoDaWork 2015   
dc.rights  This work is licensed under a Creative Commons AttributionNonCommercialNoDerivatives 4.0 International License.   
dc.subject  compound Compositions   
dc.subject  Mixing process   
dc.subject  Closure   
dc.subject  Compositional data   
dc.subject  Detection limits   
dc.title  Compound compositional data processes   
dc.type  Conference_Paper   
dc.identifier.email  BaconShone, J: johnbs@hku.hk   
dc.identifier.authority  BaconShone, J=rp00056   
dc.description.nature  postprint   
dc.identifier.hkuros  251051   