Recognizing van Deusen’s mixed estimator for annual forest inventory as a linear mixed model
2
USDA Forest Service, Rocky Mountain Research Station Forest Inventory & Analysis
Publication type: Journal Article
Publication date: 2025-01-01
scimago Q1
wos Q2
SJR: 0.621
CiteScore: 4.6
Impact factor: 1.5
ISSN: 00455067, 12086037
Abstract
The mixed estimator (ME) for annual forest inventory proposed by van Deusen (1999; Can. J. For. Res. 29: 1824–1828) is reformulated as a linear mixed model. This equivalent structure admits an interpretation of the ME as a polynomial regression on year with correlated year-specific random effects. It also uncovers the necessary criterion for maximum likelihood (ML) estimation. The improved performance of the ME under ML estimation is illustrated through simulations and application to inventory data from Montana, USA. Limitations of the ME relating to model-misspecification are also discussed.
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Affleck D. L., Gaines G. C. Recognizing van Deusen’s mixed estimator for annual forest inventory as a linear mixed model // Canadian Journal of Forest Research. 2025. Vol. 55. pp. 1-10.
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Affleck D. L., Gaines G. C. Recognizing van Deusen’s mixed estimator for annual forest inventory as a linear mixed model // Canadian Journal of Forest Research. 2025. Vol. 55. pp. 1-10.
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TY - JOUR
DO - 10.1139/cjfr-2024-0180
UR - https://cdnsciencepub.com/doi/10.1139/cjfr-2024-0180
TI - Recognizing van Deusen’s mixed estimator for annual forest inventory as a linear mixed model
T2 - Canadian Journal of Forest Research
AU - Affleck, David L.R.
AU - Gaines, George C.
PY - 2025
DA - 2025/01/01
PB - Canadian Science Publishing
SP - 1-10
VL - 55
SN - 0045-5067
SN - 1208-6037
ER -
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BibTex (up to 50 authors)
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@article{2025_Affleck,
author = {David L.R. Affleck and George C. Gaines},
title = {Recognizing van Deusen’s mixed estimator for annual forest inventory as a linear mixed model},
journal = {Canadian Journal of Forest Research},
year = {2025},
volume = {55},
publisher = {Canadian Science Publishing},
month = {jan},
url = {https://cdnsciencepub.com/doi/10.1139/cjfr-2024-0180},
pages = {1--10},
doi = {10.1139/cjfr-2024-0180}
}