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

Chien Hui Yang, Lee-Ing Tong*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)887-896
Number of pages10
JournalInternational Journal of Advanced Manufacturing Technology
Volume30
Issue number9-10
DOIs
StatePublished - 1 Sep 2006

Keywords

  • Factorial design
  • Modified maximum likelihood predictor
  • Normal distribution
  • Order statistics
  • Prediction
  • Taguchi's parameter design
  • Type II censored data

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