Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustas laika sēriju analīze× | Robustais lineārais jauktiešu modelis× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2019 | 2016 |
| Autors≠ | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition | Richardson & Welsh (robust REML); Koller (robustlmm implementation) |
| Tips≠ | Robust time series model (AR / MA / ARIMA) | Robust linear mixed-effects model |
| Pirmavots≠ | Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley. ISBN: 978-1119214687 | 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 | robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi | robust mixed-effects model, robust linear mixed model, robust LMM, Robust Karma Etkiler Modeli |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Robust Time Series Analysis fits autoregressive, moving-average, and ARIMA models to series that contain outliers or structural breaks, using M-estimation or MM-estimation instead of ordinary least squares so that a few anomalous observations do not distort the fit. It follows the robust statistics tradition consolidated in Maronna, Martin, Yohai and Salibián-Barrera (2019). | 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). |
| ScholarGateDatu kopa ↗ |
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