विधियों की तुलना करें

	विषम वितरण के लिए समायोजित बॉक्सप्लॉट	बूटस्ट्रैप अनुमान	माध्यिका निरपेक्ष विचलन (MAD) आकलन
क्षेत्र	सांख्यिकी	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model	Regression model
उद्भव वर्ष	2008	1979	1974
प्रवर्तक	Hubert & Vandervieren	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics
प्रकार	Robust outlier detection / descriptive visualization	Resampling-based inference	Robust scale estimator
मौलिक स्रोत	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.
उपनाम	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
संबंधित	5	5	5
सारांश	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.