面向重度删失可靠性数据的新型子采样策略
Novel Subsampling Strategies for Heavily Censored Reliability Data
AI总结：
针对大规模重度删失可靠性数据的计算挑战，提出基于L最优准则的高效子采样方法及对应估计算法，经真实与模拟数据验证性能优异。
摘要：
在面对海量数据时，计算能力往往存在不足，这给构建适用于大数据的统计模型或统计推断方法带来了普遍挑战。尽管子采样技术已得到广泛发展以缩减数据规模，但在应对大规模可靠性数据的独特挑战方面仍存在显著空白——这类数据的常见情况是大部分数据都存在删失。本文提出了一种适用于存在删失数据的可靠性分析的高效子采样方法，旨在估计寿命分布的参数。此外，针对仅极小比例数据为完整数据的重度删失数据，本文提出了一种新型子采样方法。文中给出了基于子采样的估计量，并推导了其渐近性质。最优子采样概率通过L最优准则推导得出，该准则最小化渐近协方差矩阵与常数矩阵乘积的迹。针对最优子采样策略依赖于全数据未知参数估计的难题，本文提出了实现所提子采样方法的高效算法。通过真实硬盘数据集案例与模拟实证研究，验证了所提方法的优越性能。
英文摘要：
Computational capability often falls short when confronted with massive data, posing a common challenge in establishing a statistical model or statistical inference method dealing with big data. While subsampling techniques have been extensively developed to downsize the data volume, there is a notable gap in addressing the unique challenge of handling extensive reliability data, in which a common situation is that a large proportion of data is censored. In this article, we propose an efficient subsampling method for reliability analysis in the presence of censoring data, intending to estimate the parameters of lifetime distribution. Moreover, a novel subsampling method for subsampling from severely censored data is proposed, i.e., only a tiny proportion of data is complete. The subsampling-based estimators are given, and their asymptotic properties are derived. The optimal subsampling probabilities are derived through the L-optimality criterion, which minimizes the trace of the product of the asymptotic covariance matrix and a constant matrix. Efficient algorithms are proposed to implement the proposed subsampling methods to address the challenge that optimal subsampling strategy depends on unknown parameter estimation from full data. Real-world hard drive dataset case and simulative empirical studies are employed to demonstrate the superior performance of the proposed methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
