Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets

Rashid, S. and Mitra, Robin and Steele, R.J. (2015) Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets. Computational Statistics and Data Analysis, 92. pp. 84-96. ISSN 0167-9473

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Abstract

Strategies for making inference in the presence of missing data after conducting a Multiple Imputation (MI) procedure are considered. An approach which approximates the posterior distribution for parameters using a mixture of tt-distributions is proposed. Simulated experiments show this approach improves inferences in some aspects, making them more stable over repeated analysis and creating narrower bounds for certain common statistics of interest. Extensions to the existing literature have been executed that provide further stability to inferences and also a strong potential to identify ways to make the analysis procedure more flexible. The competing methods have been first compared using simulated data sets and then a real data set concerning analysis of the effect of breastfeeding duration on children?s cognitive ability. R code to implement the methods used is available as online supplementary material.

Item Type:
Journal Article
Journal or Publication Title:
Computational Statistics and Data Analysis
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
123898
Deposited By:
Deposited On:
08 Mar 2018 13:20
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jan 2020 11:13