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A semiparametric method of multiple imputation

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  • Stuart R. Lipsitz
  • Lue Ping Zhao
  • Geert Molenberghs

Abstract

In this paper, we describe how to use multiple imputation semiparametrically to obtain estimates of parameters and their standard errors when some individuals have missing data. The methods given require the investigator to know or be able to estimate the process generating the missing data but requires no full distributional form for the data. The method is especially useful for non‐standard problems, such as estimating the median when data are missing.

Suggested Citation

  • Stuart R. Lipsitz & Lue Ping Zhao & Geert Molenberghs, 1998. "A semiparametric method of multiple imputation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 127-144.
    • Handle: RePEc:bla:jorssb:v:60:y:1998:i:1:p:127-144
    DOI: 10.1111/1467-9868.00113
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    Cited by:

    1. Xuerong Chen & Guoqing Diao & Jing Qin, 2020. "Pseudo likelihood‐based estimation and testing of missingness mechanism function in nonignorable missing data problems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1377-1400, December.
    2. Qihua Wang & Gregg Dinse & Chunling Liu, 2012. "Hazard function estimation with cause-of-death data missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(2), pages 415-438, April.
    3. Marco Di Zio & Ugo Guarnera, 2008. "A multiple imputation method for non-Gaussian data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 75-90.
    4. Shuanghua Luo & Changlin Mei & Cheng-yi Zhang, 2017. "Smoothed empirical likelihood for quantile regression models with response data missing at random," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(1), pages 95-116, January.
    5. Xuerong Chen & Alan T. K. Wan & Yong Zhou, 2015. "Efficient Quantile Regression Analysis With Missing Observations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 723-741, June.
    6. Shuanghua Luo & Yuxin Yan & Cheng-yi Zhang, 2024. "Two-Stage Estimation of Partially Linear Varying Coefficient Quantile Regression Model with Missing Data," Mathematics, MDPI, vol. 12(4), pages 1-15, February.
    7. Aiai Yu & Yujie Zhong & Xingdong Feng & Ying Wei, 2023. "Quantile regression for nonignorable missing data with its application of analyzing electronic medical records," Biometrics, The International Biometric Society, vol. 79(3), pages 2036-2049, September.
    8. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, eSocialSciences.

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