<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

Adjusting for Misclassification: A Three-Phase Sampling Approach

Department of Mathematics, University of Mississippi, University, MS 38677,

Roundtable Analytics, Research Triangle Park, NC 27709,

National Agricultural Statistics Service Research and Development, USDA, 1400 Independence Ave. SW, Room 6035,

EDUCAUSE, 282 Century Place, Ste 5000 Louisville, CO 80027,

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Adjusting for Misclassification: A Three-Phase Sampling Approach"}

The United States Department of Agriculture’s National Agricultural Statistics Service (NASS) conducts the June Agricultural Survey (JAS) annually. Substantial...

Adjusting for Misclassification: A Three-Phase Sampling Approach

Online veröffentlicht: 21. Feb. 2017

Seitenbereich: 207 - 222

Eingereicht: 01. Mai 2014

Akzeptiert: 01. Okt. 2016

DOI: https://doi.org/10.1515/jos-2017-0011

Schlüsselwörter
Estimation under the three-phase sampling design, nonresponse, unbiased estimator, variance estimation

© by Hailin Sang

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Adjusting for Misclassification: A Three-Phase Sampling Approach

Online veröffentlicht: 21. Feb. 2017

Seitenbereich: 207 - 222

Eingereicht: 01. Mai 2014

Akzeptiert: 01. Okt. 2016

DOI: https://doi.org/10.1515/jos-2017-0011

SchlüsselwörterEstimation under the three-phase sampling design, nonresponse, unbiased estimator, variance estimation

© by Hailin Sang

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Estimation under the three-phase sampling design, nonresponse, unbiased estimator, variance estimation