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| 기호 데이터 분석× | 구성 데이터 분석 (CoDA)× | |
|---|---|---|
| 분야≠ | 소프트 컴퓨팅 | 통계학 |
| 계열≠ | Machine learning | Regression model |
| 기원 연도≠ | 2003 | 1982 |
| 창시자≠ | Edwin Diday; Lynne Billard | John Aitchison |
| 유형≠ | Statistical framework for aggregate and set-valued data | Constrained multivariate statistical method |
| 원전≠ | Billard, 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 ↗ |
| 별칭 | SDA, Interval Data Analysis, Distributional Data Analysis, Sembolik Veri Analizi | CoDA, Simplex Analysis, Log-Ratio Analysis, Bileşim Veri Analizi |
| 관련≠ | 1 | 2 |
| 요약≠ | Symbolic 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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