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| RANSAC regresija× | Regresija običnih najmanjih kvadrata (OLS)× | Kvantilna regresija× | |
|---|---|---|---|
| Područje≠ | Statistika | Ekonometrija | Ekonometrija |
| Obitelj | Regression model | Regression model | Regression model |
| Godina nastanka≠ | 1981 | 2019 | 1978 |
| Tvorac≠ | Fischler & Bolles | Wooldridge (textbook treatment); classical least squares | Koenker & Bassett |
| Vrsta≠ | Robust linear regression | Linear regression | Conditional quantile regression |
| Temeljni izvor≠ | 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 ↗ | 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 ↗ |
| Drugi nazivi≠ | random sample consensus, RANSAC, robust regression, RANSAC Regresyonu | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Srodne | 5 | 5 | 5 |
| Sažetak≠ | RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeatedly samples small subsets, fits a candidate model, and keeps the model that wins the largest consensus of agreeing points. | 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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