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

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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.

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Acknowledgements

We are grateful to three referees and the Associate Editor for the constructive comments. This research was supported by the National Agricultural Statistics Service of the US Department of Agriculture.

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Authors and Affiliations

  1. Department of Statistics, Iowa State University, Ames, IA, 50011, USA

    Jae Kwang Kim, Zhonglei Wang & Zhengyuan Zhu

  2. National Agricultural Statistics Service, United States Department of Agriculture, Washington, DC, 20250, USA

    Nathan B. Cruze

Authors
  1. Jae Kwang Kim
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  2. Zhonglei Wang
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  3. Zhengyuan Zhu
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  4. Nathan B. Cruze
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Correspondence to Jae Kwang Kim.

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Kim, J.K., Wang, Z., Zhu, Z. et al. Combining Survey and Non-survey Data for Improved Sub-area Prediction Using a Multi-level Model. JABES 23, 175–189 (2018). https://doi.org/10.1007/s13253-018-0320-2

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  • DOI: https://doi.org/10.1007/s13253-018-0320-2

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