Abstract
Standard statistical procedures assume the availability of complete data sets. In frequent cases, however, not all values are available, and some responses may be missing due to various reasons. Rubin (1976, 1987) and Little and Rubin (2002) have discussed some concepts for handling missing data based on decision theory and models for mechanisms of nonresponse.
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© 2008 Springer-Verlag Berlin Heidelberg
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(2008). Analysis of Incomplete Data Sets. In: Linear Models and Generalizations. Springer Series in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74227-2_8
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DOI: https://doi.org/10.1007/978-3-540-74227-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74226-5
Online ISBN: 978-3-540-74227-2
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