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March 2021 An approximate best prediction approach to small area estimation for sheet and rill erosion under informative sampling
Emily Berg, Jae-Kwang Kim
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Ann. Appl. Stat. 15(1): 102-125 (March 2021). DOI: 10.1214/20-AOAS1388


The National Resources Inventory, a longitudinal survey of characteristics related to natural resources and agriculture on nonfederal U.S. land, has increasingly received requests for substate estimates in recent years. We consider estimation of erosion in subdomains of the Boone-Raccoon River Watershed. This region is of interest for its proximity to intensively cropped areas as well as important waterbodies. The NRI application requires a small area prediction approach that can handle nonlinear relationships and appropriately incorporate survey weights that may have nontrivial relationships to the response variable. Because of the informative design, the conditional distribution required to define a standard empirical Bayes predictor is unknown. We develop a prediction approach that utilizes the approximate distribution of survey weighted score equations arising from a specified two-level superpopulation model. We apply the method to construct estimates of mean erosion in small watersheds. We investigate the robustness of the procedure to an assumption of a constant dispersion parameter and validate the properties of the procedure through simulation.


The authors are grateful for the constructive comments from the reviewers and the Associate Editor. The research was partially supported by NSF Grant MMS-1733572 and a cooperative agreement between Iowa State University and USDA-NRCS.


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Emily Berg. Jae-Kwang Kim. "An approximate best prediction approach to small area estimation for sheet and rill erosion under informative sampling." Ann. Appl. Stat. 15 (1) 102 - 125, March 2021.


Received: 1 April 2020; Revised: 1 August 2020; Published: March 2021
First available in Project Euclid: 18 March 2021

Digital Object Identifier: 10.1214/20-AOAS1388

Rights: Copyright © 2021 Institute of Mathematical Statistics


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Vol.15 • No. 1 • March 2021
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