Routine multiple imputation in statistical databases

Conference Paper · October 1994with3 Reads
DOI: 10.1109/SSDM.1994.336960 · Source: IEEE Xplore
Conference: Scientific and Statistical Database Management, 1994. Proceedings., Seventh International Working Conference on

    Abstract

    This paper deals with problems concerning missing data in
    statistical databases. Multiple imputation is a statistically sound
    technique for handling incomplete data. Two problems should be addressed
    before the routine application of the technique becomes feasible. First,
    if imputations are to be appropriate for more than one statistical
    analysis, they should be generated independently of any scientific
    models that are to be applied to the data at a later stage. This is done
    by finding imputations that will extrapolate the structure of the data,
    as well as the uncertainty about this structure. A second problem is to
    use complete-data methods in an efficient way. The HERMES workstation
    encapsulates existing statistical packages in a client-server model. It
    forms a natural and convenient environment for implementing multiple
    imputation