Nonparametric predictive inference and interval probability

被引：98
|
作者：
Augustin, T
Coolen, FPA
机构：
[1] Univ Durham, Dept Math Sci, Sci Labs, Durham DH1 3LE, England
[2] Univ Munich, Dept Stat, D-80799 Munich, Germany
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2004年 / 124卷 / 02期
关键词：
A(n); capacities; conditioning; consistency; imprecise probabilities; interval probability; nonparametrics; low structure inference; predictive inference; updating;
D O I：
10.1016/j.jspi.2003.07.003
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：
The assumption A((n)), proposed by Hill (J. Amer. Statist. Assoc. 63 (1968) 677), provides a natural basis for low structure non-parametric predictive inference, and has been justified in the Bayesian framework. This paper embeds A((n))-based inference into the theory of interval probability, by showing that the corresponding bounds are totally monotone F-probability and coherent. Similar attractive internal consistency results are proven to hold for conditioning and updating. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码：251 / 272
页数：22
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