Abstract
Missing data is a common problem encountered by many practicing statisticians and engineers.
The daily rainfall data in Malaysia known as National Network (“NN”) System is no exception.
Missing data generates gaps in the data matrix which posed problems in applying standard
statistical methods to such data matrix. Thus, it is natural to replace the gaps with a probable
value with the intention of producing completed data matrix. In Malaysia, three methods of
recording daily rainfall data are used, namely manual, chart and data logger methods. Some of
these recordings may be missing for a particular day which generate incomplete unit. When all
the three recordings are missing for a particular day it generates unit missing. The current
practice is to replace the missing data with the available values when incomplete unit is
encountered. This technique is called imputation in statistical literature. However, the present
method fails to address the unit missing problem. This study introduces an alternative statistical
model which could cope with unit missing problem. The focus of this study is on Expectation
Maximization (EM) Algorithm procedure in imputing missing daily rainfall values when dealing
with incomplete units and Nearest Neighbor (NNeigh) Imputation technique when dealing with
unit missing.