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Nonparametric estimation of the mixing distribution in logistic regression mixed models with random intercepts and slopes

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  • Lesperance, Mary
  • Saab, Rabih
  • Neuhaus, John

Abstract

An algorithm that computes nonparametric maximum likelihood estimates of a mixing distribution for a logistic regression model containing random intercepts and slopes is proposed. The algorithm identifies mixing distribution support points as the maxima of the gradient function using a direct search method. The mixing proportions are then estimated through a quadratically convergent method. Two methods for computing the joint maximum likelihood estimates of the fixed effects parameters and the mixing distribution are compared. A simulation study demonstrates the performance of the algorithms and an example using National Basketball Association data is provided.

Suggested Citation

  • Lesperance, Mary & Saab, Rabih & Neuhaus, John, 2014. "Nonparametric estimation of the mixing distribution in logistic regression mixed models with random intercepts and slopes," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 211-219.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:211-219
    DOI: 10.1016/j.csda.2013.05.014
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    References listed on IDEAS

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    1. Huageng Tao & Mari Palta & Brian S. Yandell & Michael A. Newton, 1999. "An Estimation Method for the Semiparametric Mixed Effects Model," Biometrics, The International Biometric Society, vol. 55(1), pages 102-110, March.
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    Cited by:

    1. Forcina, Antonio, 2017. "A Fisher-scoring algorithm for fitting latent class models with individual covariates," Econometrics and Statistics, Elsevier, vol. 3(C), pages 132-140.
    2. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    3. Shun Yu & Xianzheng Huang, 2017. "Random-intercept misspecification in generalized linear mixed models for binary responses," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 333-359, August.

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