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| Робастний аналіз прихованих профілів× | Робастний аналіз латентних класів× | |
|---|---|---|
| Галузь | Статистика | Статистика |
| Родина | Latent structure | Latent structure |
| Рік появи≠ | 2010s | 2000s |
| Автор методу≠ | Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s) | Building on Hennig (2004) and Vermunt & Magidson (2004) |
| Тип≠ | Person-centered mixture model with robust estimation | Robust latent variable / mixture model |
| Основоположне джерело≠ | Vermunt, J. K. & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied Latent Class Analysis (pp. 89–106). Cambridge University Press. ISBN: 978-0521594035 | Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340. DOI ↗ |
| Інші назви≠ | RLPA, robust LPA, robust mixture model for continuous indicators, outlier-robust latent profile analysis | robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | Robust latent profile analysis identifies latent subgroups of individuals based on their continuous multivariate indicators while protecting parameter estimates from distortion by outliers or atypical observations. It extends standard latent profile analysis by replacing the Gaussian component densities with heavier-tailed or contaminated-normal alternatives that down-weight extreme cases during estimation. | Robust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimation, or downweighting — so that atypical response patterns do not distort the recovered class structure or class membership probabilities. |
| ScholarGateНабір даних ↗ |
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