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| Robustowe modelowanie klas ukrytych× | Solidna analiza profili utajonych× | |
|---|---|---|
| Dziedzina | Statystyka | Statystyka |
| Rodzina | Latent structure | Latent structure |
| Rok powstania≠ | 2000s | 2010s |
| Twórca≠ | Building on Hennig (2004) and Vermunt & Magidson (2004) | Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s) |
| Typ≠ | Robust latent variable / mixture model | Person-centered mixture model with robust estimation |
| Źródło pierwotne≠ | Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340. DOI ↗ | 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 |
| Inne nazwy≠ | robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis | RLPA, robust LPA, robust mixture model for continuous indicators, outlier-robust latent profile analysis |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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. | 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. |
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