手法を比較

	ブートストラップ推論	一般化最小二乗法 (GLS)
分野	統計学	統計学
系統	Regression model	Regression model
提唱年	1979	1935
提唱者	Bradley Efron	Alexander Craig Aitken
種類	Resampling-based inference	Linear estimator
原典	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
別名	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	GLS, Aitken estimator, EGLS, feasible GLS
関連	5	3
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.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.