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Fast Construction of Optimal Composite Likelihoods
- Source :
- Statistica Sinica.
- Publication Year :
- 2024
- Publisher :
- Statistica Sinica (Institute of Statistical Science), 2024.
-
Abstract
- A composite likelihood is a combination of low-dimensional likelihood objects useful in applications where the data have complex structure. Although composite likelihood construction is a crucial aspect influencing both computing and statistical properties of the resulting estimator, currently there does not seem to exist a universal rule to combine low-dimensional likelihood objects that is statistically justified and fast in execution. This paper develops a methodology to select and combine the most informative low-dimensional likelihoods from a large set of candidates while carrying out parameter estimation. The new procedure minimizes the distance between composite likelihood and full likelihood scores subject to a constraint representing the afforded computing cost. The selected composite likelihood is sparse in the sense that it contains a relatively small number of informative sub-likelihoods while the noisy terms are dropped. The resulting estimator is found to have asymptotic variance close to that of the minimum-variance estimator constructed using all the low-dimensional likelihoods.
Details
- ISSN :
- 10170405
- Database :
- OpenAIRE
- Journal :
- Statistica Sinica
- Accession number :
- edsair.doi.dedup.....aca9b32e494bb5432337a3213721ef63