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| Коригиран диаграмен бокс за асиметрични разпределения× | Джакнайф семплиране (Jackknife Resampling)× | Устойчив анализ на времеви редове× | |
|---|---|---|---|
| Област | Статистика | Статистика | Статистика |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 2008 | 1956 | 2019 |
| Създател≠ | Hubert & Vandervieren | Quenouille (1956); reviewed by Miller (1974) | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition |
| Тип≠ | Robust outlier detection / descriptive visualization | Resampling / bias and variance estimation | Robust time series model (AR / MA / ARIMA) |
| Основополагащ източник≠ | Hubert, M. & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201. 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 |
| Други названия | adjusted box plot, medcouple boxplot, skewness-adjusted boxplot, Düzeltilmiş Kutu Grafiği (Adjusted Boxplot) | 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 |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | The Adjusted Boxplot is a robust descriptive tool introduced by Hubert and Vandervieren (2008) that corrects the classical IQR-based boxplot for skewness using the medcouple statistic, reducing the false labelling of outliers in asymmetric data. | 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). |
| ScholarGateНабор от данни ↗ |
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