Latent structureMultivariate analysis
Robust Latent Class Analysis
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.
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Sources
- Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340. DOI: 10.1214/009053604000000571 ↗
- Vermunt, J. K., & Magidson, J. (2004). Latent class models. In D. Kaplan (Ed.), The Sage Handbook of Quantitative Methodology for the Social Sciences (pp. 175–198). Sage. link ↗