Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data

Juned Siddique*, Thomas R. Belin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


An Approximate Bayesian Bootstrap (ABB) offers advantages in incorporating appropriate uncertainty when imputing missing data, but most implementations of the ABB have lacked the ability to handle nonignorable missing data where the probability of missingness depends on unobserved values. This paper outlines a strategy for using an ABB to multiply impute nonignorable missing data. The method allows the user to draw inferences and perform sensitivity analyses when the missing data mechanism cannot automatically be assumed to be ignorable. Results from imputing missing values in a longitudinal depression treatment trial as well as a simulation study are presented to demonstrate the method's performance. We show that a procedure that uses a different type of ABB for each imputed data set accounts for appropriate uncertainty and provides nominal coverage.

Original languageEnglish (US)
Pages (from-to)405-415
Number of pages11
JournalComputational Statistics and Data Analysis
Issue number2
StatePublished - Dec 15 2008

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


Dive into the research topics of 'Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data'. Together they form a unique fingerprint.

Cite this