Bandingkan metode

	Inferensi Bootstrap	Estimasi Deviasi Absolut Median (MAD)	Analisis Deret Waktu Robust
Bidang	Statistika	Statistika	Statistika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1979	1974	2019
Pencetus	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics	Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition
Tipe	Resampling-based inference	Robust scale estimator	Robust time series model (AR / MA / ARIMA)
Sumber perintis	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
Terkait	5	5	5
Ringkasan	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).