Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Bayesiansk kvantilregression× | Bayesiansk Tobitmodell× | |
|---|---|---|
| Ämnesområde | Statistik | Statistik |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 2001–2011 | 1958 (classical); 1992 (Bayesian formulation) |
| Upphovsperson≠ | Kozumi & Kobayashi; building on Yu & Moyeed (2001) | James Tobin (classical Tobit, 1958); Siddhartha Chib (Bayesian Tobit, 1992) |
| Typ≠ | Bayesian semiparametric regression | Bayesian censored/limited-dependent-variable regression |
| Ursprungskälla≠ | Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578. DOI ↗ | Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26(1), 24–36. DOI ↗ |
| Alias | BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression | Bayesian censored regression, Bayesian Type I Tobit, Bayesian truncated regression, Tobit with priors |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | 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. | The Bayesian Tobit model extends Tobin's censored regression framework by replacing maximum-likelihood point estimates with a full posterior distribution over regression coefficients and error variance. By embedding Gibbs sampling with data augmentation, it produces credible intervals, handles small censored samples gracefully, and naturally incorporates prior knowledge about effect sizes. |
| ScholarGateDatamängd ↗ |
|
|