방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 베이즈 토빗 모형× | 베이즈 일반화 선형 모형× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1958 (classical); 1992 (Bayesian formulation) | 1989 (GLM); 1995 (Bayesian BDA) |
| 창시자≠ | James Tobin (classical Tobit, 1958); Siddhartha Chib (Bayesian Tobit, 1992) | McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al. |
| 유형≠ | Bayesian censored/limited-dependent-variable regression | Bayesian regression model |
| 원전≠ | Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26(1), 24–36. DOI ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| 별칭 | Bayesian censored regression, Bayesian Type I Tobit, Bayesian truncated regression, Tobit with priors | Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM |
| 관련≠ | 5 | 6 |
| 요약≠ | 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. | A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome. |
| ScholarGate데이터셋 ↗ |
|
|