Model selection approaches for predicting future order statistics from type II censored data
學年 107
學期 1
出版（發表）日期 2018-10-08
作品名稱 Model selection approaches for predicting future order statistics from type II censored data
作品名稱（其他語言）
著者 J-Y Chiang; S Wang; T-R Tsai; T Li
單位
出版者
著錄名稱、卷期、頁數 Mathematical Problems in Engineering 2018, 3465909 (29 pages)
摘要 This paper studies a discriminant problem of location-scale family in case of prediction from type II censored samples. Three model selection approaches and two types of predictors are, respectively, proposed to predict the future order statistics from censored data when the best underlying distribution is not clear with several candidates. Two members in the location-scale family, the normal distribution and smallest extreme value distribution, are used as candidates to illustrate the best model competition for the underlying distribution via using the proposed prediction methods. The performance of correct and incorrect selections under correct specification and misspecification is evaluated via using Monte Carlo simulations. Simulation results show that model misspecification has impact on the prediction precision and the proposed three model selection approaches perform well when more than one candidate distributions are competing for the best underlying distribution. Finally, the proposed approaches are applied to three data sets.
關鍵字
語言 en_US
ISSN 1563-5147
期刊性質 國外
收錄於 SCI Scopus
產學合作
通訊作者
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版,紙本
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