An Information Criterion for Auxiliary Variable Selection in Incomplete Data Analysis

An Information Criterion for Auxiliary Variable Selection in Incomplete Data Analysis

Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint d...

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Bibliographic Details
Main Authors: Shinpei Imori, Hidetoshi Shimodaira
Format: Article
Language:English
Published: MDPI AG 2019-03-01
Series:Entropy
Subjects:
Akaike information criterion
auxiliary variables
Fisher information matrix
incomplete data
Kullback–Leibler divergence
misspecification
Takeuchi information criterion
Online Access:http://www.mdpi.com/1099-4300/21/3/281

Internet

http://www.mdpi.com/1099-4300/21/3/281

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