A SURVIVAL ANALYSIS INCORPORATING AUXILIARY INFORMATION BY A BAYESIAN GENERALIZED METHOD OF MOMENTS: APPLICATION TO PURCHASE DURATION MODELING
A SURVIVAL ANALYSIS INCORPORATING AUXILIARY INFORMATION BY A BAYESIAN GENERALIZED METHOD OF MOMENTS: APPLICATION TO PURCHASE DURATION MODELING
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DOI：
10.5183/jjscs.1705001_242
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
R. Igari;T. Hoshino
中科院分区：
文献类型：
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作者：
R. Igari;T. Hoshino
在这项研究中，我们提出了一种新的估计方法，用于估计因不可忽略的无反应或缺失审查指标而导致的不完整生存数据。众所周知，如果有任何不可忽视的缺失或审查指标不能完全观察到，生存分析的结果可能是有偏差的，如Kaplan-Meier估计或Cox比例风险模型。然而，有时也会发生不可忽视的遗漏不能被指定，而审查指标从未或部分地被观察到的情况。我们提出了一种贝叶斯广义矩方法(GMM)，该方法利用种群水平的信息来识别真实的生存时间并估计参数。我们将所提出的模型应用于利用购买历史数据分析营销中的购买持续时间。
In this study, we propose a new estimation procedure for incomplete survival data caused by nonignorable nonresponses or missing censoring indicators. It is widely known that if there is any nonignorable missingness or censoring indicators cannot be fully observed, the results from survival analysis such as the Kaplan-Meier estimator or the Cox proportional hazard model may be biased. However, it sometimes occurs that nonignorable missingness cannot be specified and that the censoring indicators are never or partially observed. We propose a Bayesian generalized method of moments (GMM) approach that utilizes population-level information to identify true survival time and estimates parameters. We apply the proposed model to analyze purchase duration in marketing using purchase history data.