IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v37y2021i4p1059-1078n4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/jos-2021-0045
    Download Restriction: no
    File URL: https://libkey.io/10.2478/jos-2021-0045?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Żądło Tomasz, 2021. "On the generalisation of Quatember’s bootstrap," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 163-178, March.
    2. Tomasz Żądło, 2021. "On the generalisation of Quatember's bootstrap," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 163-178, March.
    3. Wayne A. Fuller & Jason C. Legg & Yang Li, 2017. "Bootstrap Variance Estimation for Rejective Sampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1562-1570, October.
    4. Zhonglei Wang & Liuhua Peng & Jae Kwang Kim, 2022. "Bootstrap inference for the finite population mean under complex sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1150-1174, September.
    5. Sixia Chen & David Haziza & Zeinab Mashreghi, 2022. "A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs," Stats, MDPI, vol. 5(2), pages 1-17, June.
    6. J. A. Mayor-Gallego & J. L. Moreno-Rebollo & M. D. Jiménez-Gamero, 2019. "Estimation of the finite population distribution function using a global penalized calibration method," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 1-35, March.
    7. Marie-Hélène Felt & David Laferrière, 2020. "Sample Calibration of the Online CFM Survey," Technical Reports 118, Bank of Canada.
    8. Pier Luigi Conti & Fulvia Mecatti, 2022. "Resampling under Complex Sampling Designs: Roots, Development and the Way Forward," Stats, MDPI, vol. 5(1), pages 1-12, March.
    9. 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.
    10. María del Mar Rueda & Beatriz Cobo & Antonio Arcos, 2021. "Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables," Mathematics, MDPI, vol. 9(6), pages 1-13, March.
    11. Jean-François Beaumont & Nelson Émond, 2022. "A Bootstrap Variance Estimation Method for Multistage Sampling and Two-Phase Sampling When Poisson Sampling Is Used at the Second Phase," Stats, MDPI, vol. 5(2), pages 1-19, March.
    12. Beaumont, Jean-François & Charest, Anne-Sophie, 2012. "Bootstrap variance estimation with survey data when estimating model parameters," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4450-4461.
    13. Jean-Francois Beaumont & J. N. K. Rao, 2019. "Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1071-1076, December.
    14. Marius Stefan & Michael A. Hidiroglou, 2023. "A Bootstrap Variance Procedure for the Generalised Regression Estimator," International Statistical Review, International Statistical Institute, vol. 91(2), pages 294-317, August.
    15. Heng Chen & Rallye Shen, 2017. "The Bank of Canada 2015 Retailer Survey on the Cost of Payment Methods: Calibration for Single-Location Retailers," Technical Reports 109, Bank of Canada.
    16. Daniela Marella & Paola Vicard, 2022. "Bayesian network structural learning from complex survey data: a resampling based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 981-1013, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:offsta:v:37:y:2021:i:4:p:1059-1078:n:4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.