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Symbolsk dataanalyse×Komposisjonsdataanalyse (CoDA)×
FagfeltSoft computingStatistikk
FamilieMachine learningRegression model
Opprinnelsesår20031982
OpphavspersonEdwin Diday; Lynne BillardJohn Aitchison
TypeStatistical framework for aggregate and set-valued dataConstrained multivariate statistical method
Opprinnelig kildeBillard, L., & Diday, E. (2003). From the statistics of data to the statistics of knowledge: symbolic data analysis. Journal of the American Statistical Association, 98(462), 470–487. DOI ↗Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B, 44(2), 139–177. DOI ↗
AliasSDA, Interval Data Analysis, Distributional Data Analysis, Sembolik Veri AnaliziCoDA, Simplex Analysis, Log-Ratio Analysis, Bileşim Veri Analizi
Relaterte12
SammendragSymbolic Data Analysis (SDA) is a statistical framework designed to analyze complex, aggregate, or set-valued data — called symbolic data — in which each observation represents a group or concept rather than a single scalar. Introduced in its modern statistical form by Lynne Billard and Edwin Diday in 2003, SDA extends classical statistics to handle interval-valued, histogram-valued, and multi-valued variables, enabling rigorous inference at the level of knowledge rather than raw individual records.Compositional Data Analysis (CoDA) is a branch of multivariate statistics designed for data that represent parts of a whole — proportions, percentages, or concentrations that sum to a constant. Introduced by John Aitchison in his landmark 1982 paper, CoDA recognises that standard Euclidean methods fail on the simplex and instead operates through log-ratio transformations that respect the relative nature of compositional information.
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ScholarGateSammenlign metoder: Symbolic Data Analysis · Compositional Data Analysis. Hentet 2026-06-15 fra https://scholargate.app/no/compare