Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Median Absolute Deviation (MAD) novērtējums× | Robustais lineārais jauktiešu modelis× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1974 | 2016 |
| Autors≠ | Hampel (influence-curve treatment); classical robust statistics | Richardson & Welsh (robust REML); Koller (robustlmm implementation) |
| Tips≠ | Robust scale estimator | Robust linear mixed-effects model |
| Pirmavots≠ | Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393. DOI ↗ | Koller, M. (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24. DOI ↗ |
| Citi nosaukumi | median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini | robust mixed-effects model, robust linear mixed model, robust LMM, Robust Karma Etkiler Modeli |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result. | The robust mixed model is a linear mixed-effects model for panel and repeated-measures data that tolerates outliers and heavy-tailed errors. It replaces the usual likelihood with bounded-influence estimating equations, building on the robust restricted maximum likelihood of Richardson and Welsh (1995) and the robustlmm implementation of Koller (2016). |
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