Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Analisis Klaster Robust (TCLUST)× | Estimasi MM untuk Regresi Robust× | |
|---|---|---|
| Bidang | Statistika | Statistika |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2008 | 1987 |
| Pencetus≠ | García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST) | Victor J. Yohai |
| Tipe≠ | Robust model-based clustering | Robust linear regression |
| Sumber perintis≠ | 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 ↗ |
| Alias | TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST) | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici |
| Terkait | 5 | 5 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
|
|