Computing the best linear predictor in a Hilbert space. Applications to general ARMAH processes

被引：6

作者：

Bosq, D. ^{[1
]}

机构：

[1] Univ Paris 06, Lab Stat Theor & Appl, F-75252 Paris 05, France

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2014年 / 124卷

关键词：

Functional filters; Hilbert spaces; Linear processes; Measurable linear transformations; Prediction; Large dimensions;

D O I：

10.1016/j.jmva.2013.11.013

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：

This article deals with linear prediction in large dimensions. One obtains various explicit forms of the best linear predictor in a Hilbert space. The difficulty comes from the fact that the associated linear operator is, in general, not continuous. Applications to ARMAH processes, models with noise and Bayesian estimators are considered. (C) 2013 Elsevier Inc. All rights reserved.

引用

页码：436 / 450

页数：15

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