Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Ієрархічне лінійне моделювання (ІЛМ / Багаторівневе моделювання)× | Метод головних компонент× | |
|---|---|---|
| Галузь≠ | Статистика | Машинне навчання |
| Родина≠ | Hypothesis test | Machine learning |
| Рік появи≠ | 1986 | 2002 |
| Автор методу≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Тип≠ | Parametric nested-data regression | Unsupervised dimensionality reduction |
| Основоположне джерело≠ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Інші назви≠ | HLM, MLM, multilevel modeling, multilevel analysis | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Пов'язані≠ | 4 | 3 |
| Підсумок≠ | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
| ScholarGateНабір даних ↗ |
|
|