UCLA

Existence of missing values creates a big problem in real world data. Unless those values are missing completely at random, we cannot disregard them. This paper demonstrates some implementation methods to deal with missing values. It will show the theory and implementations of EM Algorithm, Regression Imputation, Stochastic Regression Imputation and Multiple imputations. This paper begins by introducing the theories of those methods and then applying them to two examples and finally diagnostics. I will use some examples to demonstrate these algorithms and hope this helps researchers with various backgrounds solve missing values problem in their data.