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生长混合模型 (GMM)

生长混合模型(Growth Mixture Model, GMM),由Muthén和Shedden于1999年提出,是一种纵向潜在变量方法,用于识别不同的亚群体——潜在轨迹类别——每个亚群体随时间遵循自己的生长曲线。它扩展了标准的潜在生长曲线(LGC)模型,允许样本由具有不同截距、斜率和方差结构的未知类别混合而成。

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来源

  1. Muthén, B. O. & Shedden, K. (1999). Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55(2), 463–469. DOI: 10.1111/j.0006-341x.1999.00463.x

如何引用本页

ScholarGate. (2026, June 1). Growth Mixture Model. ScholarGate. https://scholargate.app/zh/statistics/growth-mixture-model

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ScholarGateGMM (Growth Mixture Model). 于 2026-06-15 检索自 https://scholargate.app/zh/statistics/growth-mixture-model · 数据集: https://doi.org/10.5281/zenodo.20539026