標題: Predicting type II censored data from factorial experiments using modified maximum likelihood predictor
作者: Yang, Chien-Hui
Tong, Lee-Ing
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: type II censored data;modified maximum likelihood predictor;factorial design;Taguchi's parameter design;prediction;order statistics;normal distribution
公開日期: 2006
摘要: 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.
URI: http://hdl.handle.net/11536/12924
http://dx.doi.org/10.1007/s00170-005-0123-9
ISSN: 0268-3768
DOI: 10.1007/s00170-005-0123-9
期刊: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume: 30
Issue: 9-10
起始頁: 887
結束頁: 896
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