Diagnosing misspecification of the random-effects distribution in mixed models
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2355
- Type
- D - Journal article
- DOI
-
10.1111/biom.12551
- Title of journal
- Biometrics
- Article number
- -
- First page
- 63
- Volume
- 73
- Issue
- 1
- ISSN
- 0006-341X
- Open access status
- Technical exception
- Month of publication
- July
- Year of publication
- 2016
- URL
-
https://e-space.mmu.ac.uk/621853/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
A - Data Science
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces a novel diagnostic methodology for detecting misspecification in mixed models which have numerous applications in longitudinal and multilevel data. Unlike existing methods, the proposed approach can be applied to the general class of mixed models. The paper formed the basis for a continuing collaboration with the biostatistics group at KU Leuven, producing 4 follow-up papers and projects. The journal announced it was one of the top 20 most downloaded papers in the 12 months after publication.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -