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Designing efficient composite likelihoods

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SINW01 - Scalable statistical inference

Composite likelihood is an inference function constructed by compounding component likelihoods based on low dimensional marginal or conditional distributions. Since the components are multiplied as if they were independent, the composite likelihood inherits the properties of likelihood inference from a misspecified model. The virtue of composite likelihood inference is “combining the advantages of likelihood with computational feasibility” (Reid, 2013). Given the wide applicability, composite likelihoods are attracting interest as scalable surrogate for intractable likelihoods. Despite the promise, application of composite likelihood is still limited by some theoretical and computational issues which have received only partial or initial responses. Open theoretical questions concern characterization of general model conditions assuring validity of composite likelihood inference, optimal selection of component likelihoods and precise evaluation of estimation uncertainty. Computational issues concern how to design composite likelihood methods to balance statistical efficiency and computational efficiency.

In this talk, after a critical review of composite likelihood theory, I shall focus on the potential merits of composite likelihood inference in modeling temporal and spatial variation of disease incidence. The talk is based on past work with Nancy Reid (Toronto) and David Firth (Warwick), and various new projects with Manuela Cattelan (Padova), Xanthi Pedeli (Venice) and Guido Masarotto (Padova).  

This talk is part of the Isaac Newton Institute Seminar Series series.

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