EM Algorithm

The EM algorithm can be used when a data set has missing data elements. The missing data is estimated using an iterative process where each iteration consists of two steps: (1) an M step (maximization) where parameters are calculated based on the missing data results from the previous E step (or via a guess in the initial iteration) and (2) an E step (expectation) where each missing data is estimated from the parameters in the previous M step.

Topics

• Multivariate normally distributed data
  • Bivariate normal data with missing elements
  • Relevant properties
  • Multivariate normal data with missing elements
  • Multivariate normal data with multiple patterns of missing data
  • Real Statistics support for multivariate data with missing data
• Independence Testing
  • Contingency tables with missing elements
  • Independence testing with missing data
  • Real Statistics support for independence testing with missing data

References

Efron and Hastie (2016) Computer age statistical inference. Cambridge University Press.
https://moodle2.units.it/pluginfile.php/340060/mod_resource/content/1/casi.pdf

Walczak, B., Massart, (2001) Dealing with missing data: Part II. Chemometrics and Intelligent Laboratory Systems 58 Ž2001. 29–42
https://www.academia.edu/59642526/Dealing_with_missing_data

Raghunathan, T. (2016) Missing data analysis in practice. CRC Press
https://www.taylorfrancis.com/books/mono/10.1201/b19428/missing-data-analysis-practice-trivellore-raghunathan

Morse, C. (2013) EM algorithm
No longer available online