Krahasoni metodat

	Diagrama Boxplot i Rregulluar për Shpërndarje të Shtrembëruara	Inferenca Bootstrap	Vlerësimi i Devijimit Mesor Absolut (MAD)
Fusha	Statistikë	Statistikë	Statistikë
Familja	Regression model	Regression model	Regression model
Viti i origjinës	2008	1979	1974
Krijuesi	Hubert & Vandervieren	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics
Lloji	Robust outlier detection / descriptive visualization	Resampling-based inference	Robust scale estimator
Burimi themelues	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.
Emërtime të tjera	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
Të lidhura	5	5	5
Përmbledhja	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.