Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beijesiskā regresija× | Latent Growth Curve Model (LGC)× | |
|---|---|---|
| Nozare≠ | Bajesa metodes | Statistika |
| Saime≠ | Bayesian methods | Latent structure |
| Izcelsmes gads≠ | — | 1990 |
| Autors≠ | — | Meredith & Tisak |
| Tips≠ | Bayesian linear model | Latent variable / longitudinal growth model |
| Pirmavots≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Meredith, W. & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122. DOI ↗ |
| Citi nosaukumi≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli |
| Saistītās≠ | 2 | 5 |
| Kopsavilkums≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | The latent growth curve model is a structural equation modelling approach introduced by Meredith and Tisak (1990) for analysing change over time. It treats each individual's starting point (intercept) and rate of change (slope) as latent variables, simultaneously estimating the average trajectory across the sample and the extent to which individuals differ in their own trajectories. |
| ScholarGateDatu kopa ↗ |
|
|