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A latent class selection model for nonignorably missing data

Author

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  • Jung, Hyekyung
  • Schafer, Joseph L.
  • Seo, Byungtae
Abstract
When we have data with missing values, the assumption that data are missing at random is very convenient. It is, however, sometimes questionable because some of the missing values could be strongly related to the underlying true values. We introduce methods for nonignorable multivariate missing data, which assume that missingness is related to the variables in question, and to the additional covariates, through a latent variable measured by the missingness indicators. The methodology developed here is useful for investigating the sensitivity of one's estimates to untestable assumptions about the missing-data mechanism. A simulation study and data analysis are conducted to evaluate the performance of the proposed method and to compare to that of MAR-based alternatives.

Suggested Citation

  • Jung, Hyekyung & Schafer, Joseph L. & Seo, Byungtae, 2011. "A latent class selection model for nonignorably missing data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 802-812, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:802-812
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    References listed on IDEAS

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    5. Andrew Gelman & Iven Van Mechelen & Geert Verbeke & Daniel F. Heitjan & Michel Meulders, 2005. "Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data," Biometrics, The International Biometric Society, vol. 61(1), pages 74-85, March.
    6. Jason Roy, 2003. "Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model," Biometrics, The International Biometric Society, vol. 59(4), pages 829-836, December.
    7. Hwan Chung & Brian P. Flaherty & Joseph L. Schafer, 2006. "Latent class logistic regression: application to marijuana use and attitudes among high school seniors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 723-743, October.
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    Cited by:

    1. Jouni Kuha & Myrsini Katsikatsou & Irini Moustaki, 2018. "Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1169-1192, October.
    2. Robitzsch, Alexander, 2020. "About Still Nonignorable Consequences of (Partially) Ignoring Missing Item Responses in Large-scale Assessment," OSF Preprints hmy45, Center for Open Science.

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