Robust likelihood-based analysis of multivariate data with missing values

Roderick Little, Hyonggin An

Research output: Contribution to journalArticlepeer-review

102 Citations (Scopus)


The model-based approach to inference from multivariate data with missing values is reviewed. Regression prediction is most useful when the covariates are predictive of the missing values and the probability of being missing, and in these circumstances predictions are particularly sensitive to model misspecification. The use of penalized splines of the propensity score is proposed to yield robust model-based inference under the missing at random (MAR) assumption, assuming monotone missing data. Simulation comparisons with other methods suggest that the method works well in a wide range of populations, with little loss of efficiency relative to parametric models when the latter are correct. Extensions to more general patterns are outlined.

Original languageEnglish
Pages (from-to)949-968
Number of pages20
JournalStatistica Sinica
Issue number3
Publication statusPublished - 2004 Jul
Externally publishedYes


  • Double robustness
  • Incomplete data
  • Penalized splines
  • Regression imputation
  • Weighting

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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