手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| MCP Penalized Regression× | 冗長性分析× | |
|---|---|---|
| 分野 | 心理測定学 | 心理測定学 |
| 系統 | Latent structure | Latent structure |
| 提唱年≠ | 2010 | 1977 |
| 提唱者≠ | Cun-Hui Zhang | Albert van den Wollenberg |
| 種類≠ | Penalized regression with minimax concave penalty | Asymmetric multivariate analysis |
| 原典≠ | Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942. DOI ↗ | van den Wollenberg, A. L. (1977). Redundancy analysis: An alternative for canonical correlation analysis. Psychometrika, 42(2), 207-219. DOI ↗ |
| 別名 | MCP | RDA |
| 関連≠ | 4 | 5 |
| 概要≠ | MCP (Minimax Concave Penalty) is a variable selection method developed by Zhang (2010) that uses a concave penalty function for automated feature selection. Like SCAD, MCP addresses bias in lasso by avoiding shrinkage of large coefficients, but uses a different penalty shape that is computationally simpler than SCAD. | Redundancy Analysis (RDA) is a multivariate technique developed by van den Wollenberg (1977) that combines multiple regression and principal component analysis. RDA finds linear combinations of predictor variables that best predict variation in response variables, making it ideal for understanding how sets of predictors collectively explain multivariate outcomes. |
| ScholarGateデータセット ↗ |
|
|