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Likelihood inference for correlated binary data without any information about the joint distributions

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  • Tsung-Shan Tsou
  • Wei-Cheng Hsiao

Abstract

We propose a universal robust likelihood that is able to accommodate correlated binary data without any information about the underlying joint distributions. This likelihood function is asymptotically valid for the regression parameter for any underlying correlation configurations, including varying under- or over-dispersion situations, which undermines one of the regularity conditions ensuring the validity of crucial large sample theories. This robust likelihood procedure can be easily implemented by using any statistical software that provides naïve and sandwich covariance matrices for regression parameter estimates. Simulations and real data analyses are used to demonstrate the efficacy of this parametric robust method.

Suggested Citation

  • Tsung-Shan Tsou & Wei-Cheng Hsiao, 2017. "Likelihood inference for correlated binary data without any information about the joint distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(5), pages 2151-2160, March.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:5:p:2151-2160
    DOI: 10.1080/03610926.2015.1033553
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