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A two-level multivariate response model for data with latent structures

Yingjuan Zhang et al.

Statistical Modelling2025https://doi.org/10.1177/1471082x241313024article
ABDC B
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0.50

What the paper says

A novel approach is proposed for analysing multilevel multivariate response data. The approach is based on identifying a one-dimensional latent variable spanning the space of responses, which then induces correlation between upper-level units. The latent variable, which can be thought of as a random effect, is estimated along with the other model parameters using an EM algorithm, which can be seen in the tradition of the 'nonparametric maximum likelihood' estimator for two-level linear (univariate response) models. Simulations and real data examples from different fields are provided to illustrate the proposed methods in the context of regression and clustering applications.

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https://doi.org/https://doi.org/10.1177/1471082x241313024

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@article{yingjuan2025,
  title        = {{A two-level multivariate response model for data with latent structures}},
  author       = {Yingjuan Zhang et al.},
  journal      = {Statistical Modelling},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/1471082x241313024},
}

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A two-level multivariate response model for data with latent structures

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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