Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Diagnostiek van invloed (Cook's distance, DFFITS, leverage)× | Hoofdcomponentenanalyse× | |
|---|---|---|
| Vakgebied≠ | Statistiek | Machine learning |
| Familie≠ | Regression model | Machine learning |
| Jaar van ontstaan≠ | 1977 | 2002 |
| Grondlegger≠ | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Type≠ | Regression diagnostic | Unsupervised dimensionality reduction |
| Oorspronkelijke bron≠ | Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Aliassen≠ | Cook's distance, DFFITS, leverage, influential observation detection | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Verwant≠ | 5 | 3 |
| Samenvatting≠ | Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
| ScholarGateGegevensset ↗ |
|
|