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首页> 外文期刊>International journal of quality, statistics, & reliability >Variable Selection Methods for Right-Censored Time-to-Event Data with High-Dimensional Covariates
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Variable Selection Methods for Right-Censored Time-to-Event Data with High-Dimensional Covariates

机译：具有高维协变量的右删失事件时间数据的变量选择方法

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摘要

Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and reliability. The proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. On the other side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application approaches, specifically in the presence of censored observations. This paper presents a multipurpose analytic model and practical nonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. In order to reduce redundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific field of application. To investigate the performance of the proposed methods, these methods are compared with recent relevant approaches through numerical experiments and simulations.
机译：技术的进步已导致在质量和可靠性等许多领域对海量和复杂数据的更大可访问性。适当地管理和利用有价值的数据可以通过预防措施显着增加知识并降低成本，而错误和误解的数据可能会导致推理和决策能力差。另一方面，在传统的应用方法中，尤其是在存在被审查的观察的情况下，处理流式高维时间到事件数据变得更加困难。本文提出了一种多用途分析模型和实用的非参数方法来分析具有高维协变量的右删失事件时间数据。为了减少冗余信息并促进实际解释，针对特定的应用领域确定故障时间的可变低效率。为了研究所提出方法的性能，通过数值实验和模拟将这些方法与最近的相关方法进行了比较。

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