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BAYESIAN COMPOSITE MARGINAL LIKELIHOODS
Francesco Pauli, Walter Racugno and Laura Ventura
Vol. 21, No. 1, Composite Likelihood Methods (January 2011), pp. 149-164
Published by: Institute of Statistical Science, Academia Sinica
Stable URL: http://www.jstor.org/stable/24309266
Page Count: 16
You can always find the topics here!Topics: Statism, Inference, Simulations, Bayesian inference, Statistics, Mathematical maxima, Approximation, Efficiency loss, Spatial models, Mathematical independent variables
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This paper proposes and discusses the use of composite marginal likelihoods for Bayesian inference. This approach allows one to deal with complex statistical models in the Bayesian framework, when the full likelihood - and thus the full posterior distribution - is impractical to compute or even analytically unknown. The procedure is based on a suitable calibration of the composite likelihood that yields the right asymptotic properties for the posterior probability distribution. In this respect, an attractive technique is offered for important settings that at present are not easily tractable from a Bayesian perspective, such as, for instance, multivariate extreme value theory. Simulation studies and an application to multivariate extremes are analysed in detail.
Statistica Sinica © 2011 Institute of Statistical Science, Academia Sinica