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Weighted pairwise likelihood estimation for a general class of random effects models

Author

Listed:
  • Vasdekis, Vassilis G. S.
  • Rizopoulos, Dimitris
  • Moustaki, Irini

Abstract

Models with random effects/latent variables are widely used for capturing unobserved heterogeneity in multilevel/hierarchical data and account for associations in multivariate data. The estimation of those models becomes cumbersome as the number of latent variables increases due to high-dimensional integrations involved. Composite likelihood is a pseudo-likelihood that combines lower-order marginal or conditional densities such as univariate and/or bivariate; it has been proposed in the literature as an alternative to full maximum likelihood estimation. We propose a weighted pairwise likelihood estimator based on estimates obtained from separate maximizations of marginal pairwise likelihoods. The derived weights minimize the total variance of the estimated parameters. The proposed weighted estimator is found to be more efficient than the one that assumes all weights to be equal. The methodology is applied to a multivariate growth model for binary outcomes in the analysis of four indicators of schistosomiasis before and after drug administration.

Suggested Citation

  • Vasdekis, Vassilis G. S. & Rizopoulos, Dimitris & Moustaki, Irini, 2014. "Weighted pairwise likelihood estimation for a general class of random effects models," LSE Research Online Documents on Economics 56733, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:56733
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    File URL: http://eprints.lse.ac.uk/56733/
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    Citations

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    Cited by:

    1. Hillary Koch & Cheryl A. Keller & Guanjue Xiang & Belinda Giardine & Feipeng Zhang & Yicheng Wang & Ross C. Hardison & Qunhua Li, 2022. "CLIMB: High-dimensional association detection in large scale genomic data," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Papageorgiou, Ioulia & Moustaki, Irini, 2019. "Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables," LSE Research Online Documents on Economics 87592, London School of Economics and Political Science, LSE Library.
    3. K. Florios & I. Moustaki & D. Rizopoulos & V. Vasdekis, 2015. "A modified weighted pairwise likelihood estimator for a class of random effects models," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 217-228, August.
    4. Marco Alfó & Francesco Bartolucci, 2015. "Latent variable models for the analysis of socio-economic data," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 151-154, August.
    5. Zhan Liu & Chun Yip Yau, 2022. "A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse," Statistical Papers, Springer, vol. 63(1), pages 317-342, February.

    More about this item

    Keywords

    categorical data; composite likelihood; generalized linear latent variable models; longitudinal data;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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