Missing data problem creates a variety of problems in many disciplines which depend on good access to accurate data and because of this reason, techniques to deal with missing data have been the subject of research in statistics, mathematics and other disciplines (Yuan, 2000; Allison, 2000; Rubin, 1978). In the engineering domain the missing data problem occurs because of a number of reasons including sensors failures, inaccessibility of measuring surfaces and so on. If the data component from the missing data is needed for decision-making purpose then it becomes very important that the missing values be estimated.

A number of methods have been proposed to deal with missing data (Little & Rubin, 2002). These include the use of Expectation-Maximization methods (Dempster, Liard, & Rubin, 1977), autoassociative neural networks (Abdella & Marwala, 2006), rough sets approach (Nelwamondo & Marwala, 2007) and many more. Wang (2008) successfully dealt with the problem of the probability density estimation in the existence of covariates when data were missing at random whereas Shih, Quan, and Chang (2008) devised a method for estimating the mean of the data which included non-ignorable missing values. Keesing et al. (2007) introduced missing data estimation procedure for 3D spiral CT image reconstruction while Wooldridge (2007) introduced inverse probability weighted estimation for general missing data problems. Kuhnen, Togneri, and Nordholm (2007) introduced the missing data estimation procedure for speech recognition that uses short-time-Fourier-transform while Mojirsheibani (2007) introduced a generalized empirical approach to non-parametric curve estimation with missing data.

In this chapter,...