NDLI: Predicting type II censored data from factorial experiments using modified maximum likelihood predictor

Content Provider	Springer Nature Link
Author	Yang, Chien Hui Tong, Lee Ing
Copyright Year	2006
Abstract	Censored data are often found in industrial experiments. The censored data are usually predicted by constructing complex statistical models or neural networks. Although a maximum likelihood predictor (MLP) was developed to predict Type II censored data, the likelihood equation may not be obtained for a closed-form solution. A modified maximum likelihood predictor (MMLP) was derived to overcome the problems of MLP. However, because MMLP requires normality assumption with unknown mean and known variance, and because the population variance of real-world experimental data is generally unknown, the MMLP has little practical use. Therefore, this study develops a modified maximum likelihood predictor (MMLP) for Type II censored data obtained from a normal distribution with unknown mean and variance. The predicted censored data using the proposed MMLP are merged with the uncensored data as a pseudo-complete data set. The analysis of variance (ANOVA) method is then employed to determine the optimal factor–level combination settings. The proposed method can also be employed to predict the Type II censored data obtained from Taguchi’s parameter designs. Two examples are given to demonstrate the proposed method and the comparisons of the proposed method with existing methods of predicting the Type II censored data are made to demonstrate the effectiveness of the proposed method.
Starting Page	887
Ending Page	896
Page Count	10
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	30
Issue Number	9-10
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-01-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Type II censored data Modified maximum likelihood predictor Factorial design Taguchi’s parameter design Prediction Order statistics Normal distribution Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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