方法对比

	经调整的箱线图（Adjusted Boxplot）	Bootstrap Inference	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	2008	1979	2019
提出者	Hubert & Vandervieren	Bradley Efron	Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition
类型	Robust outlier detection / descriptive visualization	Resampling-based inference	Robust time series model (AR / MA / ARIMA)
开创性文献	Hubert, M. & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	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	adjusted box plot, medcouple boxplot, skewness-adjusted boxplot, Düzeltilmiş Kutu Grafiği (Adjusted Boxplot)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi
相关	5	5	5
摘要	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.	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.	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).