Discount weighted estimation
Discount weighted estimation
复制标题
折扣加权估计
DOI：
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发表时间：
1984
期刊：
影响因子：
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通讯作者：
P. J. Harrison
中科院分区：
文献类型：
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作者：
J. Ameen;P. J. Harrison
指数加权回归（EWR）是一种简单易行的方法，但由于它只依赖于一个折现因子，因此在实际应用中受到限制。本文将EWR方法推广为一种称为折扣加权估计（DWE）的方法，该方法允许不同的模型成分具有不同的相关折扣因子。该方法将EWR作为一个特例。一般的非极限递归关系在实践中是有用的，特别是当从业者希望指定先验信息时，干预主观判断，并根据有限的数据依次得出估计和预测。两个定理将Dobbie和McKenzie的重要EWR极限结果推广到DWE。后者允许推导出一大类已知过程，其中DWE是最优的。通过两个应用实例说明了该方法，其中一个应用实例使用了著名的国际航空公司的乘客数据。这允许与ICI MULDO系统进行比较，该系统使用特定的两个贴现因子预测方法。另一篇论文将贴现方法扩展到贝叶斯预测、卡尔曼滤波和状态空间建模。
The parsimonious method of exponentially weighted regression (EWR) is attractive but limited in application because it depends upon just one discount factor. This paper generalizes the EWR approach to a method called discount weighted estimation (DWE) which allowed distinct model components to have different associated discount factors. The method includes EWR as a special case. The general non-limiting recurrence relationships will be useful in practice, especially when practitioners wish to specify prior information, to intervene with subjective judgement and to derive estimates and forecasts sequentially based upon limited data. Two theorems extend the important EWR limiting results of Dobbie and McKenzie to DWE. The latter permits the derivation of a large class of known processs for which DWE is optimal. The method is illustrated by two applications, one of which uses the famous international airline passenger data. This allows a comparision with the ICI MULDO system which uses a particular two discount factor forecasting method. A companion paper extends the discount methods to Bayesian forecasting, Kalman filtering and state space modelling.