Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Jaukto efektu modelis× | Neiša jauktā modeļa modelis× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1982 | 1990s–2000s (modern Bayesian MCMC era) |
| Autors≠ | Laird & Ware | Gelman, Hill, and the broader Bayesian hierarchical modeling tradition |
| Tips≠ | Mixed effects regression | Bayesian regression model |
| Pirmavots≠ | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ | Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 |
| Citi nosaukumi | LME, LMM, mixed model, random effects model | Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. | The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for both population-level and group-level effects simultaneously. |
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