方法对比
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| Bootstrap Inference× | 中位数绝对离差 (MAD) 估计× | 稳健时间序列分析× | |
|---|---|---|---|
| 领域 | 统计学 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 1979 | 1974 | 2019 |
| 提出者≠ | Bradley Efron | Hampel (influence-curve treatment); classical robust statistics | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition |
| 类型≠ | Resampling-based inference | Robust scale estimator | Robust time series model (AR / MA / ARIMA) |
| 开创性文献≠ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. 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 ↗ | 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 |
| 别名 | 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 |
| 相关 | 5 | 5 | 5 |
| 摘要≠ | 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. | 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). |
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