Linganisha mbinu

	Boxplot Iliyorekebishwa kwa Usambazaji Wenye Upotofu	Utoaji wa Hitimisho kwa Njia ya Bootstrap	Uthabiti wa Kiwango cha Thamani ya Kati ya Upotofu kamili (MAD)
Nyanja	Takwimu	Takwimu	Takwimu
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	2008	1979	1974
Mwanzilishi	Hubert & Vandervieren	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics
Aina	Robust outlier detection / descriptive visualization	Resampling-based inference	Robust scale estimator
Chanzo asilia	Hubert, M. & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.
Majina mbadala	adjusted box plot, medcouple boxplot, skewness-adjusted boxplot, Düzeltilmiş Kutu Grafiği (Adjusted Boxplot)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini
Zinazohusiana	5	5	5
Muhtasari	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.	Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.	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.