Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Иерархическая линейная модель (HLM)× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|
| Область≠ | Статистика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1992 | 2019 |
| Автор метода≠ | Bryk & Raudenbush | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Multilevel linear regression | Linear regression |
| Основополагающий источник≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Другие названия | HLM, multilevel linear model, nested data model, random coefficient model | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Связанные≠ | 4 | 5 |
| Сводка≠ | The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data. | 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). |
| ScholarGateНабор данных ↗ |
|
|