방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 로지스틱 회귀× | MM-추정량을 이용한 강건 회귀분석× | |
|---|---|---|
| 분야≠ | 연구 통계 | 통계학 |
| 계열≠ | Process / pipeline | Regression model |
| 기원 연도≠ | 1958 | 1987 |
| 창시자≠ | David Roxbee Cox | Victor J. Yohai |
| 유형≠ | Method | Robust linear regression |
| 원전≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ |
| 별칭≠ | logit model, binomial logistic regression, LR | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici |
| 관련≠ | 3 | 5 |
| 요약≠ | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. | The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved. |
| ScholarGate데이터셋 ↗ |
|
|