On specification tests for composite likelihood inference Article Swipe
Jing Huang
,
Yang Ning
,
Nancy Reid
,
Yong Chen
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/biomet/asaa039
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/biomet/asaa039
Summary 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 cases of the proposed tests and establish their limiting distributions under both null and local alternative hypotheses. Finally, we evaluate the finite-sample performance of the proposed tests in several examples.
Related Topics
Concepts
Inference
Mathematics
Likelihood function
Likelihood principle
Likelihood-ratio test
Statistical inference
Statistics
Statistical hypothesis testing
Score
Quasi-maximum likelihood
Econometrics
Computer science
Artificial intelligence
Maximum likelihood
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/biomet/asaa039
- OA Status
- green
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3035476216
All OpenAlex metadata
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https://openalex.org/W3035476216Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/biomet/asaa039Digital Object Identifier
- Title
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On specification tests for composite likelihood inferenceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
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2020-05-04Full publication date if available
- Authors
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Jing Huang, Yang Ning, Nancy Reid, Yong ChenList of authors in order
- Landing page
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https://doi.org/10.1093/biomet/asaa039Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/8232013Direct OA link when available
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Inference, Mathematics, Likelihood function, Likelihood principle, Likelihood-ratio test, Statistical inference, Statistics, Statistical hypothesis testing, Score, Quasi-maximum likelihood, Econometrics, Computer science, Artificial intelligence, Maximum likelihoodTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2022: 1Per-year citation counts (last 5 years)
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