An EM Algorithm for ML Factor Analysis with Missing Data

EM algorithm is a popular algorithm for obtaining maximum likelihood estimates. Here we propose an EM algorithm for the factor analysis model. This algorithm extends a previously proposed EM algorithm to handle problems with missing data. It is simple to implement and is the most storage efficient among its competitors. We apply our algorithm to three examples and discuss the results. For problems with reasonable amount of missing data, it converges in reasonable time. For problems with large amount of missing data EM algorithm is usually slow. For such cases we successfully apply two EM acceleration methods to our examples. Finally, we discuss different methods of obtaining standard errors and in particular we recommend a method based on center difference approximation to the derivative.

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