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Phase I Analysis of Individual Observations with Missing Data
Author(s) -
Mahmoud Mahmoud A.,
Saleh Nesma A.,
Madbuly Doaa F.
Publication year - 2014
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1508
Subject(s) - missing data , imputation (statistics) , statistics , statistic , multivariate statistics , mathematics , monte carlo method , regression , data set , regression analysis , analysis of covariance , control chart , computer science , process (computing) , operating system
The effect of the methods for handling missing values on the performance of Phase I multivariate control charts has not been investigated. In this paper, we discuss the effect of four imputation methods: mean substitution, regression, stochastic regression and the expectation maximization algorithm. Estimates of mean vector and variance covariance matrix from the treated data set are used to estimate the unknown parameters in the Hotelling's T 2 chart statistic. Based on a Monte Carlo simulation study, the performance of each of the four methods is investigated in terms of its ability to obtain the nominal in‐control and out‐of‐control overall probability of a signal. We consider three sample sizes, five levels of the percentage of missing values and three types of variable numbers. Our simulation results show that the stochastic regression method has the best overall performance among all the competing methods. Copyright © 2013 John Wiley & Sons, Ltd.