Linganisha mbinu

	Utoaji wa Hitimisho kwa Njia ya Bootstrap	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	1979	1956	2019
Mwanzilishi	Bradley Efron	Quenouille (1956); reviewed by Miller (1974)	Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition
Aina	Resampling-based inference	Resampling / bias and variance estimation	Robust time series model (AR / MA / ARIMA)
Chanzo asilia	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	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	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	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	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.	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).