Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Робастна квантильна регресія× | Байєсівська квантильна регресія× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1993–1997 | 2001–2011 |
| Автор методу≠ | Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997) | Kozumi & Kobayashi; building on Yu & Moyeed (2001) |
| Тип≠ | Robust semiparametric regression | Bayesian semiparametric regression |
| Основоположне джерело≠ | Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275 | Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578. DOI ↗ |
| Інші назви | robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR | BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression |
| Пов'язані | 6 | 6 |
| Підсумок≠ | Robust Quantile Regression estimates conditional quantiles of a response variable while simultaneously downweighting the influence of outliers. By combining the asymmetric loss function of standard quantile regression with bounded-influence or M-estimation weights, it provides reliable quantile estimates even when data contain extreme observations or heavy-tailed error distributions. | Bayesian Quantile Regression estimates the full posterior distribution of regression coefficients at any chosen quantile of the outcome. By combining the asymmetric Laplace likelihood with prior distributions over the coefficients, it delivers uncertainty-quantified estimates of conditional quantiles — such as the median, the 10th, or the 90th percentile — without assuming Gaussian errors. |
| ScholarGateНабір даних ↗ |
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