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The role played by the composite analogue of the log likelihood ratio inhypothesis testing and in setting confidence regions is not as prominent as itis in the canonical likelihood setting, since its asymptotic distributiondepends on the unknown parameter. Approximate pivots based on the composite loglikelihood ratio can be derived by using asymptotic arguments. However, theactual distribution of such pivots may differ considerably from the asymptoticreference, leading to tests and confidence regions whose levels are distantfrom the nominal ones. The use of bootstrap rather than asymptoticdistributions in the composite likelihood framework is explored. Prepivotedtests and confidence sets based on a suitable statistic turn out to be accurateand computationally appealing inferential tools.

Prepivoting composite score statistics by weighted bootstrap iteration