مقایسهٔ روش‌ها

	نمودار جعبه‌ای تعدیل‌شده برای توزیع‌های چوله	رگرسیون حداقل مربعات هرس‌شده (LTS)	برآورد انحراف مطلق میانه (MAD)
حوزه	آمار	آمار	آمار
خانواده	Regression model	Regression model	Regression model
سال پیدایش	2008	1984	1974
پدیدآور	Hubert & Vandervieren	Peter J. Rousseeuw	Hampel (influence-curve treatment); classical robust statistics
نوع	Robust outlier detection / descriptive visualization	Robust linear regression	Robust scale estimator
منبع بنیادین	Hubert, M. & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.
نام‌های دیگر	adjusted box plot, medcouple boxplot, skewness-adjusted boxplot, Düzeltilmiş Kutu Grafiği (Adjusted Boxplot)	LTS, least trimmed squares regression, trimmed least squares, robust regression	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini
مرتبط	5	5	5
خلاصه	The Adjusted Boxplot is a robust descriptive tool introduced by Hubert and Vandervieren (2008) that corrects the classical IQR-based boxplot for skewness using the medcouple statistic, reducing the false labelling of outliers in asymmetric data.	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.	Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result.