基于贝叶斯广义矩量法的包含辅助信息的生存分析:在购买持续时间模型中的应用

Journal of the Japanese Society of Computational Statistics Pub Date : 2017-12-20 DOI:10.5183/JJSCS.1705001_242

R. Igari, T. Hoshino

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引用次数: 3

摘要

在这项研究中，我们提出了一种新的估计程序，用于不可忽略的无反应或缺失审查指标导致的不完整生存数据。众所周知，如果存在不可忽略的缺失或不能完全观察到的审查指标，Kaplan-Meier估计或Cox比例风险模型等生存分析的结果可能存在偏差。然而，有时发生的情况是无法说明不可忽视的缺失，审查指标从未或部分得到遵守。我们提出了一种贝叶斯广义矩量法(GMM)方法，该方法利用种群水平信息来识别真实生存时间并估计参数。我们将提出的模型应用于利用购买历史数据分析市场营销中的购买持续时间。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A SURVIVAL ANALYSIS INCORPORATING AUXILIARY INFORMATION BY A BAYESIAN GENERALIZED METHOD OF MOMENTS: APPLICATION TO PURCHASE DURATION MODELING

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.

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Journal of the Japanese Society of Computational Statistics

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