Optimal Stratification and Allocation for the June Agricultural Survey

Lisic Jonathan (), Sang Hejian (), Zhu Zhengyuan () and Zimmer Stephanie ()
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Lisic Jonathan: Cigna, 900 Cottage Grove Rd, Bloomfield, CT 06002, USA
Sang Hejian: Iowa State University, Osborn Dr, Ames, IA 50011 USA
Zhu Zhengyuan: Iowa State University, Osborn Dr, Ames, IA 50011 USA
Zimmer Stephanie: Iowa State University, Osborn Dr, Ames, IA 50011 USA

Journal of Official Statistics, 2018, vol. 34, issue 1, 121-148

Abstract: A computational approach to optimal multivariate designs with respect to stratification and allocation is investigated under the assumptions of fixed total allocation, known number of strata, and the availability of administrative data correlated with thevariables of interest under coefficient-of-variation constraints. This approach uses a penalized objective function that is optimized by simulated annealing through exchanging sampling units and sample allocations among strata. Computational speed is improved through the use of a computationally efficient machine learning method such as K-means to create an initial stratification close to the optimal stratification. The numeric stability of the algorithm has been investigated and parallel processing has been employed where appropriate. Results are presented for both simulated data and USDA’s June Agricultural Survey. An R package has also been made available for evaluation.

Keywords: Area survey; optimal allocation; optimal stratification; multivariate design; simulated annealing (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:34:y:2018:i:1:p:121-148:n:7

DOI: 10.1515/jos-2018-0007

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