Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

3-1-2018

Journal or Book Title

Journal of Official Statistics

Volume

34

Issue

1

First Page

121

Last Page

148

DOI

10.1515/JOS-2018-0007

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.

Comments

This article is published as Lisic, Jonathan, Hejian Sang, Zhengyuan Zhu, and Stephanie Zimmer. "Optimal Stratification and Allocation for the June Agricultural Survey." Journal of Official Statistics 34, no. 1 (2018): 121-148. DOI: 10.1515/JOS-2018-0007. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Statistics Sweden

Language

en

File Format

application/pdf

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