방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 일반화 최소제곱법 (GLS)× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야≠ | 통계학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1935 | 2019 |
| 창시자≠ | Alexander Craig Aitken | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Linear estimator | Linear regression |
| 원전≠ | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 별칭≠ | GLS, Aitken estimator, EGLS, feasible GLS | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 관련≠ | 3 | 5 |
| 요약≠ | Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models. | 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). |
| ScholarGate데이터셋 ↗ |
|
|