On specification tests for composite likelihood inference.

ABSTRACT: Composite likelihood functions are often used for inference in applications where the data have a complex structure. While inference based on the composite likelihood can be more robust than inference based on the full likelihood, the inference is not valid if the associated conditional or marginal models are misspecified. In this paper, we propose a general class of specification tests for composite likelihood inference. The test statistics are motivated by the fact that the second Bartlett identity holds for each component of the composite likelihood function when these components are correctly specified. We construct the test statistics based on the discrepancy between the so-called composite information matrix and the sensitivity matrix. As an illustration, we study three important cas

SUBMITTER: Huang J 

PROVIDER: S-EPMC8232013 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature