Some imputation methods to deal with the problems of missing data in two-occasion successive sampling

Garib Nath Singh, Mohd Khalid, Jong Min Kim

    Research output: Contribution to journalArticlepeer-review

    4 Scopus citations

    Abstract

    The missing data often create nuisance to the survey practitioners in producing the reliable estimates of the desired parameters. Keeping this point in view, the present work suggests some alternative imputation techniques to deal with the missing data problem at the beginning of the analysis and proposes some estimation procedures of current population mean in two-occasion successive sampling. The properties of the suggested estimation procedures have been analyzed, and their empirical performances are compared with similar type of estimators designated for whole response situation and another estimator defined for the situation when missing observations observed in the sample data. Based on the fascinating results, suitable recommendations are put forward to the survey practitioners/researchers for their real-life practical applications.

    Original languageEnglish (US)
    Pages (from-to)557-580
    Number of pages24
    JournalCommunications in Statistics: Simulation and Computation
    Volume50
    Issue number2
    DOIs
    StatePublished - 2021

    Bibliographical note

    Funding Information:
    Authors are thankful to the reviewers for their valuable suggestions regarding the improvement of the paper. Authors are also thankful to the Indian Institute of Technology (Indian School of Mines), Dhanbad, for providing financial and necessary infrastructural support to carry out the present research work.

    Publisher Copyright:
    © 2019 Taylor & Francis Group, LLC.

    Keywords

    • Auxiliary variable
    • Bias
    • Imputation
    • Mean square error
    • Non-response
    • Optimum replacement strategy
    • Successive sampling

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