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| Ανάλυση Στιβαρής Συσταδοποίησης (TCLUST)× | Εκτίμηση MM για Ανθεκτική Παλινδρόμηση× | |
|---|---|---|
| Πεδίο | Στατιστική | Στατιστική |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 2008 | 1987 |
| Δημιουργός≠ | García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST) | Victor J. Yohai |
| Τύπος≠ | Robust model-based clustering | Robust linear regression |
| Θεμελιώδης πηγή≠ | García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A General Trimming Approach to Robust Cluster Analysis. The Annals of Statistics, 36(3), 1324-1345. DOI ↗ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ |
| Εναλλακτικές ονομασίες | TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST) | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | Robust Cluster Analysis is a trimmed model-based clustering method, introduced by García-Escudero and colleagues in 2008, that partitions continuous multivariate data into clusters while resisting the influence of outliers and noise. By setting aside a fraction of the most discordant observations, it keeps the recovered cluster structure from being contaminated by stray points. | 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. |
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