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Unified approach for regression models with nonmonotone missing at random data

Author

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  • Yang Zhao

    (University of Regina)

  • Meng Liu

    (Xiamen Golden Strait Investment Co., LTD)

Abstract

Unified approach (Chen and Chen in J R Stat Soc B 62(3):449–460, 2000) uses a working regression model to extract information from auxiliary variables in two-stage study for computing an efficient estimator of regression parameter. As far as we know, the method is limited to deal with missing complete at random data in a simple monotone missing data pattern. In this research, we extend the unified approach to estimate regression models with nonmonotone missing at random data. We describe an inverse probability weighting estimator condition on estimators from a set of working regression models which contains information from incomplete data and auxiliary variables. The proposed method is flexible and can easily accommodate incomplete data and auxiliary variables. We investigate the finite-sample performance of the proposed estimators using simulation studies and further illustrate the estimation method on a case–control study investigating the risk factors of hip fractures.

Suggested Citation

  • Yang Zhao & Meng Liu, 2021. "Unified approach for regression models with nonmonotone missing at random data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 87-101, March.
    • Handle: RePEc:spr:alstar:v:105:y:2021:i:1:d:10.1007_s10182-020-00389-y
    DOI: 10.1007/s10182-020-00389-y
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    References listed on IDEAS

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    Cited by:

    1. Yang Zhao, 2022. "Diagnostic checking of multiple imputation models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 271-286, June.

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