Using an Approximate Bayesian Bootstrap to multiply impute nonignorable missing data

Authors:
Juned Siddique;Thomas R. Belin
Affiliations:
University of Chicago, Department of Health Studies, Chicago, IL 60637, USA;University of California-Los Angeles, Department of Biostatistics, Los Angeles, CA 90095, USA
Venue:
Computational Statistics & Data Analysis
Year:
2008

Citing 2
Cited 3

Partially parametric techniques for multiple imputation

Computational Statistics & Data Analysis
On the performance of bias-reduction techniques for variance estimation in approximate Bayesian bootstrap imputation

Computational Statistics & Data Analysis

Exploiting Data Missingness in Bayesian Network Modeling

IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model

ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
An imputation method for categorical variables with application to nonlinear principal component analysis

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

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.