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

請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/121818

題名:	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
上傳時間:	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
顯示於類別:	[統計學系暨研究所] 期刊論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
index.html		0Kb	HTML	38	檢視/開啟
Model selection approaches for predicting future order statistics from type II censored data.pdf		1782Kb	Adobe PDF	31	檢視/開啟

在機構典藏中所有的資料項目都受到原著作權保護.

TAIR相關文章