Analysis of Randomly Incomplete Data Without Imputation (SpringerBriefs in Statistics 2012)
By: Tejas Desai (author) Paperback
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In this work, the theoretical results of Desai and Sen (2006, 2008) are used to describe several types of analyses of randomly incomplete data without imputation of any sort.
Contents Introduction.- Fisher Information in Randomly-Incomplete-Data Likelihoods.- Methods for Normal Data.- Methods for Categorical Data.- The General Linear Model.- Logistic Regression.- References.- Index.
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