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ブートストラップ推論×ジャックナイフ法×中央絶対偏差 (MAD) 推定×
分野統計学統計学統計学
系統Regression modelRegression modelRegression model
提唱年197919561974
提唱者Bradley EfronQuenouille (1956); reviewed by Miller (1974)Hampel (influence-curve treatment); classical robust statistics
種類Resampling-based inferenceResampling / bias and variance estimationRobust scale estimator
原典Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. DOI ↗Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393. DOI ↗
別名bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımıleave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örneklememedian absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini
関連555
概要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.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.
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ScholarGate手法を比較: Bootstrap Inference · Jackknife · MAD Estimation. 2026-06-17に以下より取得 https://scholargate.app/ja/compare