Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Конджойнт-анализ (Conjoint Analysis)× | Метод Монте-Карло× | |
|---|---|---|
| Область≠ | Планирование эксперимента | Принятие решений |
| Семейство≠ | Hypothesis test | MCDM |
| Год появления≠ | 1978 | 1949 |
| Автор метода≠ | Paul E. Green & V. Srinivasan | Metropolis, N., Ulam, S. |
| Тип≠ | Decomposition-based utility estimation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Основополагающий источник≠ | Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Другие названия≠ | CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint | — |
| Связанные≠ | 6 | 0 |
| Сводка≠ | Conjoint analysis is a preference-measurement technique that decomposes overall product evaluations into the separate utility values — called part-worths — that respondents assign to each attribute level. Formalised by Green and Srinivasan in their seminal 1978 Journal of Consumer Research paper, the method has become the dominant tool in marketing research and product design for quantifying what buyers truly trade off when they choose between options. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
| ScholarGateНабор данных ↗ |
|
|