方法证据记录
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Robust Latent Class Analysis
分类方法记录 · latent-structure / statistics
- 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. · URL
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