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| Bayesianische einfache lineare Regression× | Methode der kleinsten Quadrate (OLS)× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | Early 19th century; textbook synthesis 2013 | 2019 |
| Urheber≠ | Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al. | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Bayesian linear regression | Linear regression |
| Wegweisende Quelle≠ | 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 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Aliasnamen | Bayesian SLR, Bayesian univariate regression, probabilistic simple linear regression, Bayesian linear model | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Bayesian Simple Linear Regression models the relationship between a continuous outcome and a single predictor by combining a Gaussian likelihood with prior distributions over the intercept, slope, and error variance. The result is a full posterior distribution over all parameters, providing probabilistic uncertainty quantification rather than a single point estimate. | 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). |
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