Sammenlign metoder

	Bootstrap-inferens	Median Absolute Deviation (MAD) Estimering	Robust tidsserieanalyse
Fagfelt	Statistikk	Statistikk	Statistikk
Familie	Regression model	Regression model	Regression model
Opprinnelsesår	1979	1974	2019
Opphavsperson	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics	Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition
Type	Resampling-based inference	Robust scale estimator	Robust time series model (AR / MA / ARIMA)
Opprinnelig kilde	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.	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
Alias	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi
Relaterte	5	5	5
Sammendrag	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.	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.	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).