方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 经调整的箱线图（Adjusted Boxplot）× | 最小裁剪平方和（LTS）回归× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2008 | 1984 |
| 提出者≠ | Hubert & Vandervieren | Peter J. Rousseeuw |
| 类型≠ | Robust outlier detection / descriptive visualization | Robust linear regression |
| 开创性文献≠ | Hubert, M. & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201. DOI ↗ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ |
| 别名≠ | adjusted box plot, medcouple boxplot, skewness-adjusted boxplot, Düzeltilmiş Kutu Grafiği (Adjusted Boxplot) | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| 相关 | 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. | Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers. |
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