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Temporal-Like Bivariate Fay-Herriot Model: Leveraging Past Responses and Advanced Preprocessing for Enhanced Small Area Estimation of Growing Stock Volume

Author

Listed:
  • Aristeidis Georgakis

    (Aristotle University of Thessaloniki)

  • Vasileios E. Papageorgiou

    (Aristotle University of Thessaloniki)

  • Demetrios Gatziolis

    (USDA Forest Service, Pacific Northwest Research Station)

  • Georgios Stamatellos

    (Aristotle University of Thessaloniki)

Abstract

Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes. This study introduces an enhanced temporal-like bivariate Fay-Herriot model, improving upon its univariate counterpart. The model incorporates field data and auxiliary data, including canopy height metrics from WorldView stereo-imagery and past census data, sourced from the University Forest of Pertouli in Central Greece. The model aims to estimate the growing stock volume for 2008 and 2018, focusing on enhancing the precision of the 2018 estimates. The 2008 dependent variable is used as auxiliary information by the model for more reliable 2018 small area estimates. A novel preprocessing pipeline is also introduced, which includes outlier identification, cluster analysis, and variance smoothing. Compared to direct estimates and the standard univariate Fay-Herriot model, our bivariate approach shows a percentage variance reduction of 96.58% and 13.52%, respectively. The methodology not only offers more reliable estimates with reduced variance and bias but also contributes to more accurate decision-making for sustainable forest management.

Suggested Citation

  • Aristeidis Georgakis & Vasileios E. Papageorgiou & Demetrios Gatziolis & Georgios Stamatellos, 2024. "Temporal-Like Bivariate Fay-Herriot Model: Leveraging Past Responses and Advanced Preprocessing for Enhanced Small Area Estimation of Growing Stock Volume," SN Operations Research Forum, Springer, vol. 5(1), pages 1-28, March.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:1:d:10.1007_s43069-023-00288-3
    DOI: 10.1007/s43069-023-00288-3
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    References listed on IDEAS

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    1. Marhuenda, Yolanda & Molina, Isabel & Morales, Domingo, 2013. "Small area estimation with spatio-temporal Fay–Herriot models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 308-325.
    2. Elaheh Torkashvand & Mohammad Jafari Jozani & Mahmoud Torabi, 2017. "Clustering in small area estimation with area level linear mixed models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1253-1279, October.
    3. Benavent, Roberto & Morales, Domingo, 2016. "Multivariate Fay–Herriot models for small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 372-390.
    4. F. Mauro & I. Molina & A. García‐Abril & R. Valbuena & E. Ayuga‐Téllez, 2016. "Remote sensing estimates and measures of uncertainty for forest variables at different aggregation levels," Environmetrics, John Wiley & Sons, Ltd., vol. 27(4), pages 225-238, June.
    5. Hao Sun & Emily Berg & Zhengyuan Zhu, 2022. "Bivariate small‐area estimation for binary and gaussian variables based on a conditionally specified model," Biometrics, The International Biometric Society, vol. 78(4), pages 1555-1565, December.
    6. Innocent Ngaruye & Joseph Nzabanita & Dietrich von Rosen & Martin Singull, 2017. "Small area estimation under a multivariate linear model for repeated measures data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(21), pages 10835-10850, November.
    7. Roberto Benavent & Domingo Morales, 2021. "Small area estimation under a temporal bivariate area-level linear mixed model with independent time effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 195-222, March.
    8. Francisco Mauro & Vicente J Monleon & Hailemariam Temesgen & Kevin R Ford, 2017. "Analysis of area level and unit level models for small area estimation in forest inventories assisted with LiDAR auxiliary information," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-14, December.
    9. Jan Pablo Burgard & Domingo Morales & Anna-Lena Wölwer, 2022. "Small area estimation of socioeconomic indicators for sampled and unsampled domains," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 287-314, June.
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