AI LIMIT THEORY FOR BIVARIATE CENTRAL AND BIVARIATE INTERMEDIATE DUAL GENERALIZED ORDER STATISTICS

被引：0
|
作者：
Barakat, H. M. [1 ]
Nigm, E. M. [1 ]
Abd Elgawad, M. A. [2 ]
机构：
[1] Zagazig Univ, Fac Sci, Dept Math, Zagazig, Egypt
[2] Benha Univ, Fac Sci, Dept Math, Banha, Egypt
来源：
PROBABILITY AND MATHEMATICAL STATISTICS-POLAND | 2015年 / 35卷 / 02期
关键词：
Dual generalized order statistics; dual generalized central order statistics; dual generalized intermediate order statistics;
D O I：
暂无
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Burkschat et al. (2003) have introduced the concept of dual generalized order statistics (dgos) to unify several models that produce descendingly ordered random variables (rv's) like reversed order statistics, lower k-records and lower Pfeifer records. In this paper we derive the limit distribution functions (df's) of bivariate central and bivariate intermediate m-dgos. It is revealed that the convergence of the marginals of the m-dgos implies the convergence of the joint df. Moreover, we derive the conditions under which the asymptotic independence between the two marginals occurs.
引用
收藏
页码：267 / 284
页数：18
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