Regression model
RANSAC回归
RANSAC回归是一种鲁棒的线性回归方法，由Fischler和Bolles于1981年提出，它通过自动排除异常值来拟合数据集中内点模型。它不是一次性拟合所有数据，而是反复采样小样本，拟合候选模型，并保留获得最大数量一致点的模型。
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Method map
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来源
- Fischler, M. A. & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24(6), 381-395. DOI: 10.1145/358669.358692 ↗
- Torr, P. H. S. & Zisserman, A. (2000). MLESAC: A New Robust Estimator with Application to Estimating Image Geometry. Computer Vision and Image Understanding, 78(1), 138-156. DOI: 10.1006/cviu.1999.0832 ↗
如何引用本页
ScholarGate. (2026, June 1). Random Sample Consensus (RANSAC) Regression. ScholarGate. https://scholargate.app/zh/statistics/ransac-regression
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 最小裁剪平方和（LTS）回归统计学↔ compare
- 普通最小二乘法 (OLS) 回归计量经济学↔ compare
- 分位数回归计量经济学↔ compare
- 稳健协方差估计 (MCD)统计学↔ compare
- Theil-Sen 估计器统计学↔ compare