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Health Services and Outcomes Research Methodology

Published in:

01-06-2012

Using AIC in multiple linear regression framework with multiply imputed data

Authors: Ashok Chaurasia, Ofer Harel

Published in: Health Services and Outcomes Research Methodology | Issue 2-3/2012

Abstract

Many model selection criteria proposed over the years have become common procedures in applied research. However, these procedures were designed for complete data. Complete data is rare in applied statistics, in particular in medical, public health and health policy settings. Incomplete data, another common problem in applied statistics, introduces its own set of complications in light of which the task of model selection can get quite complicated. Recently, few have suggested model selection procedures for incomplete data with varying degrees of success. In this paper we explore model selection by the Akaike Information Criterion (AIC) in the multivariate regression setting with ignorable missing data accounted for via multiple imputation.

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Title: Using AIC in multiple linear regression framework with multiply imputed data
Authors: Ashok Chaurasia
Ofer Harel
Publication date: 01-06-2012
Publisher: Springer US
Published in: Health Services and Outcomes Research Methodology / Issue 2-3/2012
Print ISSN: 1387-3741
Electronic ISSN: 1572-9400
DOI: https://doi.org/10.1007/s10742-012-0088-8

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Using AIC in multiple linear regression framework with multiply imputed data

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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