Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Bāziskā lineārā regresija× | Parastā mazāko kvadrātu (OLS) regresija× | |
|---|---|---|
| Nozare≠ | Bajesa metodes | Ekonometrija |
| Saime≠ | Bayesian methods | Regression model |
| Izcelsmes gads≠ | 2013 (modern reference); foundations 18th–19th century | 2019 |
| Autors≠ | Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al. | Wooldridge (textbook treatment); classical least squares |
| Tips≠ | Bayesian linear model | Linear regression |
| Pirmavots≠ | 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 |
| Citi nosaukumi≠ | bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Bayesian linear regression is a probabilistic extension of the ordinary linear model, introduced through Bayes' rule and formalised in its modern computational workflow by Gelman et al. (2013). Rather than returning a single point estimate for each coefficient, it combines a user-specified prior distribution with the likelihood of the observed data to produce a full posterior distribution over all parameters, from which credible intervals and posterior predictive distributions are derived. | 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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