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

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.

KEYWORDS:

• Correlated binary data
• Binomial model
• Logistic regression
• Robust likelihood
• Model misspecification.

MATHEMATICS SUBJECT CLASSIFICATION:

• 62J12
• 62F35

Funding

This work is supported by grant NSC 100-2118-M-008-001-MY2 of Ministry of Science and Technology, Taiwan, R.O.C.