Note on information bias and efficiency of composite likelihood
read the original abstract
Does the asymptotic variance of the maximum composite likelihood estimator of a parameter of interest always decrease when the nuisance parameters are known? Will a composite likelihood necessarily become more efficient by incorporating addi- tional independent component likelihoods, or by using component likelihoods with higher dimension? In this note we show through illustrative examples that the an- swer to both questions is no, and indeed the opposite direction might be observed. The role of information bias is highlighted to understand the occurrence of these paradoxical phenomenon.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Composite likelihood inference for the Poisson log-normal model
A composite-likelihood EM algorithm with importance sampling yields computationally feasible, asymptotically valid inference for the Poisson log-normal model on moderately large multivariate count datasets.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.