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2001-7367
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Evaluation of Generalized Variance Functions in the Analysis of Complex Survey Data

MoonJung Cho
  • Corresponding author
  • U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. N.E., Washington, DC 20212, U.S.A
  • Email:
/ John L. Eltinge
  • Corresponding author
  • U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. N.E., Washington, DC 20212, U.S.A
  • Email:
/ Julie Gershunskaya
  • Corresponding author
  • U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. N.E., Washington, DC 20212, U.S.A
  • Email:
/ Larry Huff
  • Corresponding author
  • U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. N.E., Washington, DC 20212, U.S.A
  • Email:
Published Online: 2014-02-14 | DOI: https://doi.org/10.2478/jos-2014-0004

Abstract

Two sets of diagnostics are presented to evaluate the properties of generalized variance functions (GVFs) for a given sample survey. The first set uses test statistics for the coefficients of multiple regression forms of GVF models. The second set uses smoothed estimators of the mean squared error (MSE) of GVF-based variance estimators. The smooth version of the MSE estimator can provide a useful measure of the performance of a GVF estimator, relative to the variance of a standard design-based variance estimator. Some of the proposed methods are applied to sample data from the Current Employment Statistics survey.

Keywords: Complex sample design; degrees of freedom; design-based inference; model-based inference; quarterly census of employment and wages; superpopulation model; U.S. current employment statistics (CES) survey; variance estimator stability

References

  • Abramowitz, M. and Stegun, I.A. (1972). Handbook of Mathematical Functions. New York: Dover Publications, INC.

  • Binder, D.A. (1983). On the Variances of Asymptotically Normal Estimators from Complex Surveys. International Statistical Review, 51, 279-292.

  • Bureau Of Labor Statistics (1997). Employment, Hours, and Earnings from the Establishment Survey, Chapter 2 of BLS Handbook of Methods, U.S. Department of Labor.

  • Butani, S., Stamas, G., and Brick, M. (1997). Sample Redesign for the Current Employment Statistics Survey. JSM Proceedings, Survey Research Methods Section, Alexandria, VA: American Statistical Association, 517-522.

  • Cho, M.J., Eltinge, J.L., Gershunskaya, J., and Huff, L. (2002). Evaluation of the Predictive Precision of Generalized Variance Functions in the Analysis of Complex Survey Data. In JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association, 534-539. Available at: http://www.amstat.org/sections/SRMS/Proceedings/ (accessed September 19, 2013).

  • Cleveland, W.S. and Grosse, E. (1991). Computational Methods for Local Regression. Statistics and Computing, 1, 47-62. [Web of Science]

  • Corbeil, R.R. and Searle, S.R. (1976). Restricted Maximum Likelihood (REML) Estimation of Variance Components in the Mixed Model. Technometrics, 18, 31-38.

  • Davidian, M., Carroll, R.J., and Smith, W. (1988). Variance Functions and the Minimum Detectable Concentration in Assays. Biometrika, 75, 549-556. DOI: http://www.dx.doi.org/10.1093/biomet/75.3.549 [Crossref]

  • Draper, N.R. and Smith, H. (1998). Applied Regression Analysis, (Third Edition). New York: Wiley.

  • Eltinge, J.L., Fields, R.C.,Gershunskaya, J.,Getz, P.,Huff, L., Tiller, R., andWaddington,D. (2001). Small Domain Estimation in the Current Employment Statistics Program, Unpublished Background Material for the FESAC Session on Small Domain Estimation at the Bureau of Labor Statistics.

  • Fuller, W.A. (1987). Measurement Error Models. New York: Wiley.

  • Gershunskaya, J. and Lahiri, P. (2005). Variance Estimation for Domains in the U.S. Current Employment Statistics Program. In JSM Proceedings, Survey Research Methods Section, Alexandria, VA: American Statistical Association, 3044-3051. Available at: http://www.amstat.org/sections/SRMS/Proceedings/ (accessed September 19, 2013).

  • Harville, D.A. (1977). Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems. Journal of the American Statistical Association, 71, 320-338. DOI: http://www.dx.doi.org/10.1080/01621459.1977.10480998 [Crossref]

  • Johnson, E.G. and King, B.F. (1987). Generalized Variance Functions for a Complex Sample Survey. Journal of Official Statistics, 3, 235-250. [Web of Science]

  • Judkins, D.R. (1990). Fay’s Method for Variance Estimation. Journal of Official Statistics, 6, 223-239.

  • Karlberg, F. (2000). Survey Estimation for Highly Skewed Population in the Presence of Zeros. Journal of Official Statistics, 16, 229-241.

  • Kendall, M.G. and Stuart, A. (1968). Advanced Theory of Statistics 3. New York: Hafner Publishing Company.

  • Korn, E.L. and Graubard, B.I. (1990). Simultaneous Testing of Regression Coefficients with Complex Survey Data: Use of Bonferroni t Statistics. The American Statistician, 44, 270-276.

  • O’Malley, A.J. and Zaslavsky, A.M. (2005). Variance-Covariance Functions for Domain Means of Ordinal Survey Items. Survey Methodology, 31, 169-182.

  • Patterson, H.D. and Thompson, R. (1971). Recovery of Inter-Block Information When Block Sizes Are Unequal. Biometrika, 58, 545-554.

  • Satterthwaite, F.E. (1946). An Approximate Distribution of Estimates of Variance Components. Biometrics Bulletin, 2, 110-114.

  • Skinner, C.J. (1986). Design Effects in Two-Stage Sampling. Journal of Royal Statistical Society, Series B, 48, 89-99.

  • U.S. Bureau Of Labor Statistics (2006). Employment & Earnings, U.S. Department of Labor, 53(8).

  • U.S. Bureau Of Labor Statistics (2011). Employment, Hours, and Earnings from the Establishment Survey, Chapter 2 of BLS Handbook of Methods, U.S. Department of Labor, Available at: http://www.bls.gov/opub/hom/pdf/homch2.pdf (accessed September 19, 2013).

  • Valliant, R. (1987). Generalized Variance Functions in Stratified Two-Stage Sampling. Journal of American Statistical Association, 82, 499-508.

  • Werking, G. (1997). Overview of the CES redesign. In JSM Proceedings, the Section on Survey Research Methods: American Statistical Association, 512-516. Available at: http://www.amstat.org/sections/SRMS/Proceedings/ (accessed September 19, 2013).

  • Wolter, K.M. (2007). Introduction to Variance Estimation (Second Edition). New York: Springer-Verlag.

About the article

Published Online: 2014-02-14

Published in Print: 2014-03-01


Citation Information: Journal of Official Statistics, ISSN (Online) 2001-7367, DOI: https://doi.org/10.2478/jos-2014-0004. Export Citation

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