방법 비교

	부트스트랩 추론	잭나이프 재표본 추출	강건 시계열 분석
분야	통계학	통계학	통계학
계열	Regression model	Regression model	Regression model
기원 연도	1979	1956	2019
창시자	Bradley Efron	Quenouille (1956); reviewed by Miller (1974)	Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition
유형	Resampling-based inference	Resampling / bias and variance estimation	Robust time series model (AR / MA / ARIMA)
원전	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
별칭	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
관련	5	5	5
요약	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).