Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robustais lineārais jauktiešu modelis× | Parastā mazāko kvadrātu (OLS) regresija× | |
|---|---|---|
| Nozare≠ | Statistika | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2016 | 2019 |
| Autors≠ | Richardson & Welsh (robust REML); Koller (robustlmm implementation) | Wooldridge (textbook treatment); classical least squares |
| Tips≠ | Robust linear mixed-effects model | Linear regression |
| Pirmavots≠ | Koller, M. (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Citi nosaukumi | robust mixed-effects model, robust linear mixed model, robust LMM, Robust Karma Etkiler Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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). | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateDatu kopa ↗ |
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