方法对比

	成分数据分析 (CoDA)	多元线性回归	符号数据分析
领域	统计学	统计学	软计算
方法族	Regression model	Regression model	Machine learning
起源年份	1982	1886	2003
提出者	John Aitchison	Francis Galton; formalized by Karl Pearson	Edwin Diday; Lynne Billard
类型	Constrained multivariate statistical method	Parametric linear model	Statistical framework for aggregate and set-valued data
开创性文献	Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B, 44(2), 139–177.	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.	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.
别名	CoDA, Simplex Analysis, Log-Ratio Analysis, Bileşim Veri Analizi	MLR, OLS regression, multiple regression, linear regression with multiple predictors	SDA, Interval Data Analysis, Distributional Data Analysis, Sembolik Veri Analizi
相关	2	8	1
摘要	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.	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.	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.