Statistical analysis of incomplete long-range dependent data

被引：8
|
作者：
Palma, W [1 ]
Del Pino, G [1 ]
机构：
[1] Pontificia Univ Catolica Chile, Dept Estadist, Santiago 22, Chile
来源：
BIOMETRIKA | 1999年 / 86卷 / 04期
关键词：
ARFIMA model; incomplete data; linear predictor; long-memory; maximum likelihood; mean square prediction error; state space system;
D O I：
10.1093/biomet/86.4.965
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
This paper addresses both theoretical and methodological issues related to the prediction of long-memory models with incomplete data. Estimates and forecasts are calculated by means of state space models and the influence of data gaps on the performance of short and long run predictions is investigated. These techniques are illustrated with a statistical analysis of the minimum water levels of the Nile river, a time series exhibiting strong dependency.
引用
收藏
页码：965 / 972
页数：8
相关论文
共 50 条
12345