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| Bootstrapinferens× | Jackknife-resampling× | Robust tidsserieanalys× | |
|---|---|---|---|
| Ämnesområde | Statistik | Statistik | Statistik |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 1979 | 1956 | 2019 |
| Upphovsperson≠ | Bradley Efron | Quenouille (1956); reviewed by Miller (1974) | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition |
| Typ≠ | Resampling-based inference | Resampling / bias and variance estimation | Robust time series model (AR / MA / ARIMA) |
| Ursprungskälla≠ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. 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 |
| Alias | 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 |
| Närliggande | 5 | 5 | 5 |
| Sammanfattning≠ | 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). |
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