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Inference for clustered data using the independence loglikelihood

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  • Richard E. Chandler
  • Steven Bate

Abstract

We use the properties of independence estimating equations to adjust the 'independence' loglikelihood function in the presence of clustering. The proposed adjustment relies on the robust sandwich estimator of the parameter covariance matrix, which is easily calculated. The methodology competes favourably with established techniques based on independence estimating equations; we provide some insight as to why this is so. The adjustment is applied to examples relating to the modelling of wind speed in Europe and annual maximum temperatures in the U.K. Copyright 2007, Oxford University Press.

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  • Richard E. Chandler & Steven Bate, 2007. "Inference for clustered data using the independence loglikelihood," Biometrika, Biometrika Trust, vol. 94(1), pages 167-183.
    • Handle: RePEc:oup:biomet:v:94:y:2007:i:1:p:167-183
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    File URL: http://hdl.handle.net/10.1093/biomet/asm015
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    Cited by:

    1. Padoan, Simone A. & Bevilacqua, Moreno, 2015. "Analysis of Random Fields Using CompRandFld," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i09).
    2. Gourieroux, C. & Monfort, A., 2018. "Composite indirect inference with application to corporate risks," Econometrics and Statistics, Elsevier, vol. 7(C), pages 30-45.
    3. Costa, Rui J. & Wilkinson-Herbots, Hilde M., 2021. "Inference of gene flow in the process of speciation: Efficient maximum-likelihood implementation of a generalised isolation-with-migration model," Theoretical Population Biology, Elsevier, vol. 140(C), pages 1-15.
    4. Chuan Hong & Georgia Salanti & Sally C. Morton & Richard D. Riley & Haitao Chu & Stephen E. Kimmel & Yong Chen, 2020. "Testing small study effects in multivariate meta‐analysis," Biometrics, The International Biometric Society, vol. 76(4), pages 1240-1250, December.
    5. Paolo Vidoni, 2018. "A note on predictive densities based on composite likelihood methods," METRON, Springer;Sapienza Università di Roma, vol. 76(1), pages 31-48, April.
    6. Fred Espen Benth & Jūratė Šaltytė Benth, 2012. "Modeling and Pricing in Financial Markets for Weather Derivatives," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8457, August.
    7. Nuo Xi & Michael W. Browne, 2014. "Contributions to the Underlying Bivariate Normal Method for Factor Analyzing Ordinal Data," Journal of Educational and Behavioral Statistics, , vol. 39(6), pages 583-611, December.
    8. Elsa Vazquez & Jeffrey R. Wilson, 2021. "Partitioned method of valid moment marginal model with Bayes interval estimates for correlated binary data with time-dependent covariates," Computational Statistics, Springer, vol. 36(4), pages 2701-2718, December.
    9. Hung‐pin Lai & Subal C. Kumbhakar, 2020. "Estimation of a dynamic stochastic frontier model using likelihood‐based approaches," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 217-247, March.
    10. Duha Hamed & Ahmad Alzaghal, 2021. "New class of Lindley distributions: properties and applications," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-22, December.
    11. Kaitlyn Cook & Wenbin Lu & Rui Wang, 2023. "Marginal proportional hazards models for clustered interval‐censored data with time‐dependent covariates," Biometrics, The International Biometric Society, vol. 79(3), pages 1670-1685, September.
    12. Mevin Hooten & Christopher Wikle & Michael Schwob, 2020. "Statistical Implementations of Agent‐Based Demographic Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 441-461, August.
    13. Meisam Moghimbeygi & Mousa Golalizadeh, 2019. "A longitudinal model for shapes through triangulation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 99-121, March.
    14. L. L. Henn, 2022. "Limitations and performance of three approaches to Bayesian inference for Gaussian copula regression models of discrete data," Computational Statistics, Springer, vol. 37(2), pages 909-946, April.
    15. F. Giummolè & V. Mameli & E. Ruli & L. Ventura, 2019. "Objective Bayesian inference with proper scoring rules," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 728-755, September.
    16. Jūratė Šaltytė Benth & Laura Šaltytė, 2011. "Spatial--temporal model for wind speed in Lithuania," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(6), pages 1151-1168, April.
    17. Fang Han & Wei Pan, 2012. "A Composite Likelihood Approach to Latent Multivariate Gaussian Modeling of SNP Data with Application to Genetic Association Testing," Biometrics, The International Biometric Society, vol. 68(1), pages 307-315, March.
    18. Kaitlyn Cook & Rui Wang, 2021. "Estimation of conditional power for cluster‐randomized trials with interval‐censored endpoints," Biometrics, The International Biometric Society, vol. 77(3), pages 970-983, September.
    19. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    20. Tata Subba Rao & Granville Tunnicliffe Wilson & Joao Jesus & Richard E. Chandler, 2017. "Inference with the Whittle Likelihood: A Tractable Approach Using Estimating Functions," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 204-224, March.

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