Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель со смешанными эффектами× | Обобщенная линейная модель (GLM)× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1982 | 1972 |
| Автор метода≠ | Laird & Ware | John A. Nelder & Robert W. M. Wedderburn |
| Тип≠ | Mixed effects regression | Regression framework |
| Основополагающий источник≠ | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ |
| Другие названия | LME, LMM, mixed model, random effects model | GLM, generalized regression, exponential family regression, link-function model |
| Связанные≠ | 4 | 6 |
| Сводка≠ | 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 Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. |
| ScholarGateНабор данных ↗ |
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