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| Hồi quy Bình phương Nhỏ nhất Cắt tỉa (Least Trimmed Squares - LTS)× | Hồi quy Bình phương Tối thiểu Thông thường (OLS)× | Hồi quy Quantile× | |
|---|---|---|---|
| Lĩnh vực≠ | Thống kê | Kinh tế lượng | Kinh tế lượng |
| Họ | Regression model | Regression model | Regression model |
| Năm ra đời≠ | 1984 | 2019 | 1978 |
| Người khởi xướng≠ | Peter J. Rousseeuw | Wooldridge (textbook treatment); classical least squares | Koenker & Bassett |
| Loại≠ | Robust linear regression | Linear regression | Conditional quantile regression |
| Công trình gốc≠ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Tên gọi khác≠ | LTS, least trimmed squares regression, trimmed least squares, robust regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Liên quan | 5 | 5 | 5 |
| Tóm tắt≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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