Latent structureMultivariate analysis
稳健潜在剖面分析 (Robust Latent Profile Analysis)
稳健潜在剖面分析在识别个体潜在亚群时，基于其连续多元指标，同时保护参数估计免受异常值或典型性差的观测值的影响。它通过将高斯分量密度替换为具有更重尾部或污染正态分布的替代模型，在估计过程中降低极端个案的权重，从而扩展了标准潜在剖面分析。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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
- Punzo, A. & McNicholas, P. D. (2016). Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model. Journal of Classification, 33(2), 293–331. DOI: 10.1007/s00357-017-9234-x ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Latent Profile Analysis. ScholarGate. https://scholargate.app/zh/statistics/robust-latent-profile-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 潜在类别分析 (Latent Class Analysis, LCA)统计学↔ compare
- 潜剖面分析 (Latent Profile Analysis, LPA)心理测量学↔ compare
- 混合模型统计学↔ compare
- 稳健潜类别分析统计学↔ compare
- 鲁棒混合模型拟合统计学↔ compare