TABLE OF CONTENTS In this chapter, the traditional missing data imputation issues such as missing data patterns and mechanisms are described. Attention is paid to the best models to deal with particular missing data mechanisms. A review of traditional missing data imputation methods, namely case deletion and prediction rules, is conducted. For case deletion, list-wise and pair-wise deletions are reviewed. In addition, for prediction rules, the imputation techniques such as mean substitution, hot-deck, regression and decision trees are also reviewed. Two missing data examples are studied, namely: the Sudoku puzzle and a mechanical system. The major conclusions drawn from these examples are that there is a need for an accurate model that describes inter-relationships and rules that define the data and that a good optimization method is required for a successful missing data estimation procedure.
Datasets are frequently characterized by their incompleteness. There are a number of reasons why data become missing (Ljung, 1989). These include sensor failures, omitted entries in databases and non-response in questionnaires. In many situations, data collectors put in place firm measures to circumvent any incompleteness in data gathering. Nevertheless, it is unfortunate that despite all these efforts, data incompleteness remains a major problem in data analysis (Beunckens, Sotto, & Molenberghs, 2008; Schafer, 1997; Schafer & Olsen, 1998). The specific reason for the incompleteness of data is usually not known in advance, particularly in engineering problems. Consequently, methods for averting missing data are normally not successful. The absence of complete data then hampers decision-making processes because of...
