Vertaile menetelmiä
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| Monimuuttujainen multippeli lineaarinen regressio× | Harjanneregressio× | |
|---|---|---|
| Tieteenala≠ | Tilastotiede | Koneoppiminen |
| Menetelmäperhe≠ | Regression model | Machine learning |
| Syntyvuosi≠ | 2007 | 1970 |
| Kehittäjä≠ | Johnson & Wichern (textbook treatment); classical multivariate least squares | Hoerl, A.E. & Kennard, R.W. |
| Tyyppi≠ | Multivariate linear regression | L2-regularized linear regression |
| Alkuperäislähde≠ | Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson. ISBN: 978-0131877153 | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Rinnakkaisnimet | multivariate multiple regression, MLR with multiple dependent variables, multiple-outcome regression, Çok Değişkenli Regresyon (MLR — Çoklu DV) | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | Multivariate regression is a linear regression method that predicts several continuous dependent variables at the same time from a shared set of predictors. As developed in standard treatments such as Johnson and Wichern's Applied Multivariate Statistical Analysis (2007), each response equation can be fitted by ordinary least squares while the covariance structure of the residuals is used for joint testing across outcomes. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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