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강건 ANOVA (Welch 및 절사 평균)×최소제곱법(OLS) 회귀×테일-센 추정량×
분야통계학계량경제학통계학
계열Regression modelRegression modelRegression model
기원 연도195120191968
창시자Welch (1951); robust trimmed-mean approach popularised by WilcoxWooldridge (textbook treatment); classical least squaresHenri Theil (1950); P. K. Sen (1968)
유형Robust one-way analysis of varianceLinear regressionRobust linear regression
원전Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336. DOI ↗Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389. DOI ↗
별칭Welch ANOVA, trimmed-mean ANOVA, heteroscedastic one-way ANOVA, Robust ANOVA (Welch & Trimmed Mean)ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonuTheil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator
관련556
요약Robust ANOVA compares the central tendency of three or more groups when the classical assumptions of normality and equal variances fail. It combines Welch's heteroscedasticity-adjusted statistic, introduced by Welch in 1951, with trimmed-mean tests advanced by Wilcox, giving reliable comparisons in the presence of outliers and unequal group spreads.Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%.
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ScholarGate방법 비교: Robust ANOVA · OLS Regression · Theil-Sen Estimator. 2026-06-18에 다음에서 검색함: https://scholargate.app/ko/compare