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Estimation of Domain Means from Business Surveys in the Presence of Stratum Jumpers and Nonresponse

Author

Listed:
  • Xu Mengxuan

    (Institute for Work and Health, 400 University Avenue, Suite 1800, Toronto, Ontario M5G 1S5, Canada.)

  • Landsman Victoria

    (Institute for Work and Health, 400 University Avenue, Suite 1800, Toronto, Ontario M5G 1S5, Canada.)

  • Graubard Barry I.

    (National Cancer Institute, Division of Cancer Epidemiology and Genetics, Bethesda, MD 20852, U.S.A.)

Abstract

Misclassified frame records (also called stratum jumpers) and low response rates are characteristic for business surveys. In the context of estimation of the domain parameters, jumpers may contribute to extreme variation in sample weights and skewed sampling distributions of the estimators, especially for domains with a small number of observations. There is limited literature about the extent to which these problems may affect the performance of the ratio estimators with nonresponse-adjusted weights. To address this gap, we designed a simulation study to explore the properties of the Horvitz-Thompson type ratio estimators, with and without smoothing of the weights, under different scenarios. The ratio estimator with propensity-adjusted weights showed satisfactory performance in all scenarios with a high response rate. For scenarios with a low response rate, the performance of this estimator improved with an increase in the proportion of jumpers in the domain. The smoothed estimators that we studied performed well in scenarios with non-informative weights, but can become markedly biased when the weights are informative, irrespective of response rate. We also studied the performance of the ’doubled half’ bootstrap method for variance estimation. We illustrated an application of the methods in a real business survey.

Suggested Citation

  • Xu Mengxuan & Landsman Victoria & Graubard Barry I., 2021. "Estimation of Domain Means from Business Surveys in the Presence of Stratum Jumpers and Nonresponse," Journal of Official Statistics, Sciendo, vol. 37(4), pages 1059-1078, December.
    • Handle: RePEc:vrs:offsta:v:37:y:2021:i:4:p:1059-1078:n:4
    DOI: 10.2478/jos-2021-0045
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    References listed on IDEAS

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    1. Erika Antal & Yves Tillé, 2014. "A new resampling method for sampling designs without replacement: the doubled half bootstrap," Computational Statistics, Springer, vol. 29(5), pages 1345-1363, October.
    2. Jean‐François Beaumont & Zdenek Patak, 2012. "On the Generalized Bootstrap for Sample Surveys with Special Attention to Poisson Sampling," International Statistical Review, International Statistical Institute, vol. 80(1), pages 127-148, April.
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