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| 일반화 선형 모형 (GLM)× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야≠ | 통계학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1972 | 2019 |
| 창시자≠ | John A. Nelder & Robert W. M. Wedderburn | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Regression framework | Linear regression |
| 원전≠ | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 별칭 | GLM, generalized regression, exponential family regression, link-function model | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 관련≠ | 6 | 5 |
| 요약≠ | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. | 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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