Warranty Claim Forecasting Based On Weighted Maximum Likelihood Estimation
Warranty Claim Forecasting Based On Weighted Maximum Likelihood Estimation
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DOI：
10.1002/qre.1399
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发表时间：
2012-10-01
影响因子：
2.3
通讯作者：
Wu, Shaomin
中科院分区：
文献类型：
--
作者：
Akbarov, Artur;Wu, Shaomin
近几个月报告的保修索赔可能包含比前几个月报告的更多最新信息。因此，使用加权最大似然估计来估计模型参数可能会比最大似然估计带来更好的保修预测模型性能。本文研究了这个问题，并对泊松过程和 ARIMA 模型等参数模型与人工神经网络等非参数模型的预测性能进行了比较。它表明混合非齐次泊松过程模型可以比其他竞争方法带来更好的预测结果。该论文还表明，使用加权最大似然估计建立的模型比基于最大似然估计的模型产生的误差更小。版权所有 (C) 2012 约翰·威利父子有限公司
Warranty claims reported in recent months might carry more up to date information than those reported in earlier months. Using weighted maximum likelihood estimation for estimating model parameters might therefore lead to better performance of warranty forecasting models than maximum likelihood estimation. This paper examines this issue and also presents comparison of the forecasting performance of the parametric models such as Poisson processes and ARIMA models and non parametric models such as artificial neural networks. It shows that mixed non homogenous Poisson process models can lead to better forecasting results than other competing methods. The paper also shows that the models built with the weighted maximum likelihood estimation yield smaller error than those based on the maximum likelihood estimation. Copyright (C) 2012 John Wiley & Sons, Ltd.