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Parameter estimation and hypothesis tests in logistic model for complex correlated data

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  • Mou, Keyi
  • Li, Zhiming
  • Cheng, Jinlong

Abstract

Observations are frequently generated in clinical trials from correlated multiple organs (or parts) of individuals. The statistical inference is little about conducting regression analysis based on such data. This paper first develops a logistic regression for correlated multiple responses using a stable correlation binomial (SCB) model. Then, we obtain maximum likelihood estimators (MLEs) of unknown parameters through a fast quadratic lower bound (QLB) algorithm. Further, likelihood ratio, score and Wald statistics are used to test the effect of covariates based on the MLEs. Finally, the QLB algorithm and asymptotic tests are evaluated through simulations and applied to real dental data.

Suggested Citation

  • Mou, Keyi & Li, Zhiming & Cheng, Jinlong, 2025. "Parameter estimation and hypothesis tests in logistic model for complex correlated data," Statistics & Probability Letters, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:stapro:v:217:y:2025:i:c:s0167715224002633
    DOI: 10.1016/j.spl.2024.110294
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    References listed on IDEAS

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    1. Gary Witt, 2014. "A Simple Distribution for the Sum of Correlated, Exchangeable Binary Data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(20), pages 4265-4280, October.
    2. Tian, Guo-Liang & Tang, Man-Lai & Liu, Chunling, 2012. "Accelerating the quadratic lower-bound algorithm via optimizing the shrinkage parameter," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 255-265.
    3. Jan Kalina & Patrik Janáček, 2023. "Testing exchangeability of multivariate distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(15), pages 3142-3156, November.
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