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| 구성 데이터 분석 (CoDA)× | 기호 데이터 분석× | |
|---|---|---|
| 분야≠ | 통계학 | 소프트 컴퓨팅 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 1982 | 2003 |
| 창시자≠ | John Aitchison | Edwin Diday; Lynne Billard |
| 유형≠ | Constrained multivariate statistical method | Statistical framework for aggregate and set-valued data |
| 원전≠ | Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B, 44(2), 139–177. DOI ↗ | 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 ↗ |
| 별칭 | CoDA, Simplex Analysis, Log-Ratio Analysis, Bileşim Veri Analizi | SDA, Interval Data Analysis, Distributional Data Analysis, Sembolik Veri Analizi |
| 관련≠ | 2 | 1 |
| 요약≠ | 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. | 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. |
| ScholarGate데이터셋 ↗ |
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