Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-25T18:45:33.172Z Has data issue: false hasContentIssue false
  • English
  • Français

A Note on the Reliability Tests of Estimates from ARMS Data

Published online by Cambridge University Press:  15 September 2016

C. S. Kim ,
C. Hallahan ,
W. Lindamood ,
G. Schaible  and
J. Payne
C. S. Kim
Affiliation:
U.S. Department of Agriculture, Economic Research Service, Washington, DC
C. Hallahan
Affiliation:
U.S. Department of Agriculture, Economic Research Service, Washington, DC
W. Lindamood
Affiliation:
U.S. Department of Agriculture, Economic Research Service, Washington, DC
G. Schaible
Affiliation:
U.S. Department of Agriculture, Economic Research Service, Washington, DC
J. Payne
Affiliation:
U.S. Department of Agriculture, Economic Research Service, Washington, DC
Get access Rights & Permissions [Opens in a new window]

Abstract

USDA uses the concept of “publish-ability” rather than statistical reliability of an estimate for quality validation of USDA estimates, which is solely based on the sample size and the coefficient of variation (CV). We demonstrate conceptually how the reliability of the sample mean can be tested by estimating the upper and lower bounds of the confidence interval for an unknown population mean using the CV. However, the reliability test for the sample mean can be made only under the normality assumption. USDA multiple-way Agricultural Resource Management Survey (ARMS) estimates are used to illustrate the relative measure of precision for sample-based estimators.

Keywords

ARMS datacoefficient of variationpublish-abilityreliability
Type
Articles
Information
Agricultural and Resource Economics Review , Volume 33 , Issue 2 , October 2004 , pp. 293 - 297
Copyright
Copyright © 2004 Northeastern Agricultural and Resource Economics Association 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barlow, R. E., and Proschan, F. (1965). Mathematical Theory of Reliability. New York: John Wiley and Sons, Inc.Google Scholar
Dubman, R. W. (2000). “Variance Estimation with USDA's Farm Costs and Return Surveys and Agricultural Resource Management Study Surveys.” Staff Report No. AGES 00-01, USDA/Economic Research Service, Washington, DC.Google Scholar
Dwass, M. (1970). Probability and Statistics. New York: W. A. Benjamin, Inc.Google Scholar
Efron, B. (1982). “The Jackknife, the Bootstrap, and Other Resampling Plans.” Society for Industrial and Applied Mathematics, Philadelphia, PA. Google Scholar
Kott, P. S. (1997). “Statistical Analysis with a Delete-a-Group Jackknife.” Unpublished paper, USDA/National Agricultural Statistics Service, Washington, DC.Google Scholar
Kott, P. S. (2001, July). “Using the Delete-a-Group Jackknife Variance Estimator in NASS Surveys.” Revised Research Report No. RD-98-01, USDA/National Agricultural Statistics Service, Washington, DC.Google Scholar
Kott, P. S. (2003). Chief Statistician, National Agricultural Statistics Service, USDA, Washington, DC. Personal communication.Google Scholar
Miller, R. G. (1964). “A Trustworthy Jackknife.” Annals of Mathematical Statistics 39, 1594–1605.Google Scholar
Parzen, E. (1960). Modern Probability Theory and Its Applications. New York: John Wiley and Sons, Inc.Google Scholar
Sommer, J. E., Hoppe, R. A., Green, R. C., and Korb, P. J. (1998, December). “Structural and Financial Characteristics of U.S. Farms, 1995: 20th Annual Family Farm Report to Congress.” Agriculture Information Bulletin No. 746, USDA/Economic Research Service, Washington, DC. [108 pp.]. Online. Available at http://www.ers.usda.gov/publications/aib746/aib746f.pdf.Google Scholar
U.S. Department of Agriculture. (1993, September 23). Joint Policy on Variance Estimation and Statistical Reporting Standards on NHANES III and CSFII Reports: HNIS/NCHS Analytic Working Group Recommendations. Washington, DC.Google Scholar