IDEAS home Printed from https://ideas.repec.org/a/isa/journl/v16y2011i1-2p45-64.html
   My bibliography  Save this article

Methods for variance estimation under random hot deck imputation in business surveys

Author

Listed:
  • Paolo Righi

    (Italian National Institute of Statistics)

  • Stefano Falorsi

    (Italian National Institute of Statistics)

  • Andrea Fasulo

    (Italian National Institute of Statistics)

Abstract

When the imputed values are treated as if they were observed the precision of the estimates is generally overstated. In the paper three variance methods under imputatation are taken into account. Two of them are the well known bootstrap and Multiple Imputation. The third is a new method based on grouped jackknife easy to implement, not computer intensive and suitable when random hot deck imputation is performed. A simulative comparison on real business data has been carried out. The findings show that the proposed method has good performances with respect to the other ones.

Suggested Citation

  • Paolo Righi & Stefano Falorsi & Andrea Fasulo, 2014. "Methods for variance estimation under random hot deck imputation in business surveys," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 16(1-2), pages 45-64.
    • Handle: RePEc:isa:journl:v:16:y:2011:i:1-2:p:45-64
    as

    Download full text from publisher

    File URL: http://www.istat.it/it/files/2014/10/Articolo-4_Methodos-for-variance....pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Saigo, H. & Sitter, R.R., 2005. "Jackknife variance estimator with reimputation for randomly imputed survey data," Statistics & Probability Letters, Elsevier, vol. 73(3), pages 321-331, July.
    2. Jae Kwang Kim, 2004. "Fractional hot deck imputation," Biometrika, Biometrika Trust, vol. 91(3), pages 559-578, September.
    3. Jae Kwang Kim & J. Michael Brick & Wayne A. Fuller & Graham Kalton, 2006. "On the bias of the multiple‐imputation variance estimator in survey sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 509-521, June.
    4. Kott, Phillip S., 2001. "Using the Delete-a-Group Jackknife Variance Estimator in NASS Surveys," NASS Research Reports 235089, United States Department of Agriculture, National Agricultural Statistics Service.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shu-Hui Hsieh & Shen-Ming Lee & Chin-Shang Li, 2022. "A Two-stage Multilevel Randomized Response Technique With Proportional Odds Models and Missing Covariates," Sociological Methods & Research, , vol. 51(1), pages 439-467, February.

    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. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.
    2. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    3. Chen, Sixia & Haziza, David, 2023. "A unified framework of multiply robust estimation approaches for handling incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    4. Toni P. Miles & Changle Li & M. Mahmud Khan & Rana Bayakly & Deborah Carr, 2023. "Estimating Prevalence of Bereavement, Its Contribution to Risk for Binge Drinking, and Other High-Risk Health States in a State Population Survey, 2019 Georgia Behavioral Risk Factor Surveillance Surv," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
    5. Yijie Xue & Nicole Lazar, 2012. "Empirical likelihood-based hot deck imputation methods," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 629-646.
    6. Luca Sartore & Kelly Toppin & Linda Young & Clifford Spiegelman, 2019. "Developing Integer Calibration Weights for Census of Agriculture," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(1), pages 26-48, March.
    7. Młodak Andrzej, 2021. "An application of a complex measure to model–based imputation in business statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 1-28, March.
    8. Lambert, Dayton M. & Sullivan, Patrick, 2006. "Conservation Reserve Program Participation and Acreage Enrollment of Working Farms," 2006 Annual meeting, July 23-26, Long Beach, CA 21361, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Gelein, Brigitte & Haziza, David & Causeur, David, 2014. "Preserving relationships between variables with MIVQUE based imputation for missing survey data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 197-208.
    10. Dong, Fengxia & Mitchell, Paul D., 2023. "Economic and risk analysis of sustainable practice adoption among U.S. corn growers," Agricultural Systems, Elsevier, vol. 211(C).
    11. Parcel Joshua D. & Schroeter John R. & Azzam Azzeddine M, 2017. "A Re-Examination of Multistage Economies in Hog Farming," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 15(2), pages 1-15, December.
    12. Shaun R. Seaman & Ian R. White & Andrew J. Copas & Leah Li, 2012. "Combining Multiple Imputation and Inverse-Probability Weighting," Biometrics, The International Biometric Society, vol. 68(1), pages 129-137, March.
    13. Sullivan, Danielle & Andridge, Rebecca, 2015. "A hot deck imputation procedure for multiply imputing nonignorable missing data: The proxy pattern-mixture hot deck," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 173-185.
    14. Lu Chen & Luca Sartore & Habtamu Benecha & Valbona Bejleri & Balgobin Nandram, 2022. "Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs," Stats, MDPI, vol. 5(3), pages 1-18, September.
    15. Danhyang Lee & Jae Kwang Kim, 2022. "Semiparametric imputation using conditional Gaussian mixture models under item nonresponse," Biometrics, The International Biometric Society, vol. 78(1), pages 227-237, March.
    16. Musser, Wesley N. & Lambert, Dayton M. & Daberkow, Stan G., 2006. "Factors Affecting Direct and Indirect Energy Use in U.S. Corn Production," 2006 Annual meeting, July 23-26, Long Beach, CA 21063, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    17. Helene Boistard & Guillaume Chauvet & David Haziza, 2016. "Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 683-699, September.
    18. Rashid, S. & Mitra, R. & Steele, R.J., 2015. "Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 84-96.
    19. Qin, Yongsong & Rao, J.N.K. & Ren, Qunshu, 2008. "Confidence intervals for marginal parameters under fractional linear regression imputation for missing data," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1232-1259, July.
    20. Ton de Waal & Wieger Coutinho, 2017. "Preserving Logical Relations while Estimating Missing Values," Romanian Statistical Review, Romanian Statistical Review, vol. 65(3), pages 47-59, September.

    More about this item

    Keywords

    Bootstrap; Multiple Imputation; Jacknife; Extended DAGJK; Replicate weights; Monte Carlo simulation.;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

    Statistics

    Access and download statistics

    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:isa:journl:v:16:y:2011:i:1-2:p:45-64. 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: Stefania Rossetti (email available below). General contact details of provider: https://edirc.repec.org/data/istgvit.html .

    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.