Linganisha mbinu

	Boxplot Iliyorekebishwa kwa Usambazaji Wenye Upotofu	Mbinu ya kusampuli upya ya jackknife	Uchambuzi Imara wa Mfululizo wa Wakati
Nyanja	Takwimu	Takwimu	Takwimu
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	2008	1956	2019
Mwanzilishi	Hubert & Vandervieren	Quenouille (1956); reviewed by Miller (1974)	Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition
Aina	Robust outlier detection / descriptive visualization	Resampling / bias and variance estimation	Robust time series model (AR / MA / ARIMA)
Chanzo asilia	Hubert, M. & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201.	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley. ISBN: 978-1119214687
Majina mbadala	adjusted box plot, medcouple boxplot, skewness-adjusted boxplot, Düzeltilmiş Kutu Grafiği (Adjusted Boxplot)	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi
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.	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.	Robust Time Series Analysis fits autoregressive, moving-average, and ARIMA models to series that contain outliers or structural breaks, using M-estimation or MM-estimation instead of ordinary least squares so that a few anomalous observations do not distort the fit. It follows the robust statistics tradition consolidated in Maronna, Martin, Yohai and Salibián-Barrera (2019).