Evaluating Estimation Methods of Missing Data on a Multivariate Process CapabilityIndex

Quality of products has been one of the most important issues for manufacturers in the recent decades.One of the challenging issues is evaluating capability of the process using process capability indices.On the other hand, usually the missing data is available in many manufacturing industries. So far, theperformance of estimation methods of missing data on process capability indices has not beenevaluated. Hence, we analyze the performance of a process capability index when we deal with themissing data. For this purpose, we consider a multivariate process capability index and evaluate fourmethods including Mean Substitution, EM algorithm, Regression Imputation and StochasticRegression Imputation to estimate missing data. In the analysis, factors including percent of missingdata (k), sample size (m), correlation coefficients (r ) and the estimation methods of missing data areinvestigated. We evaluate the main and interaction effects of the factors on response variable which isdefined as difference between the estimated index and the computed index with full data by usingGeneral Linear Model in ANOVA table. The results of this research show that the StochasticRegression Imputation has the best performance among the estimation methods and the percent ofmissing data (k) has the highest effect on response variable. Also, we conclude that the sample size hasthe lowest effect on response variable among the mentioned factors.