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Peer-reviewedLicence not statedJournal of Statistical Computation and Simulation

A unifying theory for the evaluation of conditional Akaike information for mixed-effects models

  • Nan Zheng1
  • Noel G. Cadigan2
  • James T. Thorson3
  1. 1Memorial University of Newfoundland
  2. 2Centre for Fisheries Ecosystems Research, Fisheries and Marine Institute of Memorial University of Newfoundland, St. John's, Canada
  3. 3Resource Ecology and Fisheries Management, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, USA
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Cite

BibTeXRIS
BibTeX
@article{zheng2026unifying,
  title = {{A unifying theory for the evaluation of conditional Akaike information for mixed-effects models}},
  author = {Nan Zheng and Noel G. Cadigan and James T. Thorson},
  journal = {Journal of Statistical Computation and Simulation},
  year = {2026},
  month = sep,
  doi = {10.1080/00949655.2026.2736799},
  url = {https://arxiv.org/abs/2411.14185},
}
RIS
TY  - JOUR
TI  - A unifying theory for the evaluation of conditional Akaike information for mixed-effects models
AU  - Nan Zheng
AU  - Noel G. Cadigan
AU  - James T. Thorson
PY  - 2026
DA  - 2026/09/22
JO  - Journal of Statistical Computation and Simulation
DO  - 10.1080/00949655.2026.2736799
UR  - https://arxiv.org/abs/2411.14185
SN  - 0094-9655
SN  - 1026-7778
SN  - 1563-5163
ER  -