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| 稳健潜在剖面分析 (Robust Latent Profile Analysis)× | 混合模型× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 2010s | 1894 |
| 提出者≠ | Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s) | Karl Pearson |
| 类型≠ | Person-centered mixture model with robust estimation | Latent variable / density estimation |
| 开创性文献≠ | 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 | McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268 |
| 别名 | RLPA, robust LPA, robust mixture model for continuous indicators, outlier-robust latent profile analysis | finite mixture model, mixture distribution model, FMM, model-based clustering |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. | Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components. |
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