Linear Models pp 203-227 | Cite as

Analysis of Incomplete Data Sets

  • Calyampudi Radhakrishna Rao
  • Helge Toutenburg
Part of the Springer Series in Statistics book series (SSS)

Abstract

Standard statistical procedures assume the availability of complete data sets. In sample surveys or censuses, some of the individuals may not respond to some or all items being asked. In such cases missing data may have a strong influence on the statistical analysis of the remaining data set. Rubin (1976, 1987) and Little and Rubin (1987) have discussed some concepts for handling missing data based on decision theory and models for mechanism of nonresponse.

Keywords

Unbiased Estimator Complete Case Analysis Random Subsample Miss Data Mechanism Bootstrap Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Calyampudi Radhakrishna Rao
    • 1
  • Helge Toutenburg
    • 2
  1. 1.Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Institut für StatistikUniversität MünchenMünchenGermany

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