Distances between models of generalized order statistics

JOURNAL OF MULTIVARIATE ANALYSIS

卷:118

Article

Vuong, Q. N.¹  Bedbur, S.¹  Kamps, U.¹ 

[1] Rhein Westfal TH Aachen, Inst Stat, D-52056 Aachen, Germany

关键词: Order statistics;    Sequential order statistics;    Progressive type-II censoring;    Kullback-Leibler divergence;    Jeffreys-Kullback-Leibler distance;    Renyi divergence;    Cressie-Read power divergence;    Hellinger metric;   

DOI  :  10.1016/j.jmva.2013.03.010

来源: Elsevier

【 摘 要 】

The concept of generalized order statistics is a distribution theoretical set-up, which contains a variety of models for ordered data as particular cases, such as common order statistics, sequential order statistics, progressively type-II censored order statistics, record values, kth record values, and Pfeifer record values. In order to quantify the structure of generalized order statistics, distances. between different respective models are measured by means of explicit expressions for divergences and distances applied to joint densities of ordered random variables. The results are exemplarily utilized to find a closest common order statistics model to some given model of sequential order statistics. Moreover, statistical applications in reliability are shown. (C) 2013 Elsevier Inc. All rights reserved.

【 授权许可】

Free