Adjusting for Misclassification: A Three-Phase Sampling Approach

Hailin Sang; Kenneth K. Lopiano; Denise A. Abreu; Andrea C. Lamas; Pam Arroway; Linda J. Young

doi:10.1515/jos-2017-0011

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Adjusting for Misclassification: A Three-Phase Sampling Approach

Journal of Official Statistics

Volume 33 (2017): Issue 1 (March 2017)

By: Hailin Sang, Kenneth K. Lopiano, Denise A. Abreu, Andrea C. Lamas, Pam Arroway and Linda J. Young

Open Access

|Feb 2017

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Abstract

The United States Department of Agriculture’s National Agricultural Statistics Service (NASS) conducts the June Agricultural Survey (JAS) annually. Substantial misclassification occurs during the prescreening process and from field-estimating farm status for nonresponse and inaccessible records, resulting in a biased estimate of the number of US farms from the JAS. Here, the Annual Land Utilization Survey (ALUS) is proposed as a follow-on survey to the JAS to adjust the estimates of the number of US farms and other important variables. A three-phase survey design-based estimator is developed for the JAS-ALUS with nonresponse adjustment for the second phase (ALUS). A design-unbiased estimator of the variance is provided in explicit form.

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Articles in this issue

DOI: https://doi.org/10.1515/jos-2017-0011 | Journal eISSN: 2001-7367

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Language: English

Page range: 207 - 222

Submitted on: May 1, 2014

Accepted on: Oct 1, 2016

Published online: Feb 21, 2017

Published in print: Mar 1, 2017

Published by: Sciendo

In partnership with: Paradigm Publishing Services

Keywords:

Estimation under the three-phase sampling design,

Related subjects:

Probability and statistics

© 2017 Hailin Sang, Kenneth K. Lopiano, Denise A. Abreu, Andrea C. Lamas, Pam Arroway, Linda J. Young, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 33 (2017): Issue 1 (March 2017)