Optimal Stratification and Allocation for the June Agricultural Survey

Date
2018-03-01
Authors
Lisic, Jonathan
Sang, Hejian
Zhu, Zhengyuan
Zimmer, Stephanie
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
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.

Description

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.

Keywords
Area survey, optimal allocation, optimal stratification, multivariate design, simulated annealing
Citation
DOI
Collections