Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mbinu Imara ya Mraba Midogo Iliyopimwa (Robust WLS)× | Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | Regression ya Kiasi (Quantile Regression)× | |
|---|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model | Regression model |
| Mwaka wa asili≠ | 1964/1981 | 2019 | 1978 |
| Mwanzilishi≠ | Huber, P. J. | Wooldridge (textbook treatment); classical least squares | Koenker & Bassett |
| Aina≠ | Robust weighted regression | Linear regression | Conditional quantile regression |
| Chanzo asilia≠ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 | 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 ↗ |
| Majina mbadala≠ | robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Zinazohusiana | 5 | 5 | 5 |
| Muhtasari≠ | Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates. | 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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