Model selection approaches for predicting future order statistics from type II censored data

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/121818

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题名:	Model selection approaches for predicting future order statistics from type II censored data
作者:	Chiang, J-Y;Wang, S;Tsai, T-R;Li, T
日期:	2018-10-08
上传时间:	2021-12-29 12:10:42 (UTC+8)
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.
關聯:	Mathematical Problems in Engineering 2018, 3465909 (29 pages)
DOI:	10.1155/2018/3465909
显示于类别:	[統計學系暨研究所] 期刊論文

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