Missing Data: A Comparison of Neural Network and Expectation Maximisation Techniques

Understand

The estimation of missing input vector elements in real time processing applications requires a system that possesses the knowledge of certain characteristics such as correlations between variables, which are inherent in the input space.

• Computational intelligence techniques and maximum likelihood techniques do possess such characteristics and as a result are important for imputation of missing data.
• This paper compares two approaches to the problem of missing data estimation.
• The first technique is based on the current state of the art approach to this problem, that being the use of Maximum Likelihood (ML) and Expectation Maximisation (EM.