Combining Survey and Non-survey Data for Improved Sub-area Prediction Using a Multi-level Model

Jae Kwang Kim (), Zhonglei Wang, Zhengyuan Zhu and Nathan B. Cruze
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Jae Kwang Kim: Iowa State University
Zhonglei Wang: Iowa State University
Zhengyuan Zhu: Iowa State University
Nathan B. Cruze: United States Department of Agriculture

Journal of Agricultural, Biological and Environmental Statistics, 2018, vol. 23, issue 2, No 1, 175-189

Abstract: Abstract Combining information from different sources is an important practical problem in survey sampling. Using a hierarchical area-level model, we establish a framework to integrate auxiliary information to improve state-level area estimates. The best predictors are obtained by the conditional expectations of latent variables given observations, and an estimate of the mean squared prediction error is discussed. Sponsored by the National Agricultural Statistics Service of the US Department of Agriculture, the proposed model is applied to the planted crop acreage estimation problem by combining information from three sources, including the June Area Survey obtained by a probability-based sampling of lands, administrative data about the planted acreage and the cropland data layer, which is a commodity-specific classification product derived from remote sensing data. The proposed model combines the available information at a sub-state level called the agricultural statistics district and aggregates to improve state-level estimates of planted acreages for different crops. Supplementary materials accompanying this paper appear on-line.

Keywords: Agricultural survey; Hierarchical model; Mean squared prediction error; Small area estimation; Survey integration (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13253-018-0320-2

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