随机删失数据的非参数检验

Q1 Decision Sciences

Annals of Data Science Pub Date : 2023-10-26 DOI:10.1007/s40745-023-00500-5

Ayushee, Narinder Kumar, Manish Goyal

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引用次数: 0

摘要

本文提出了一种非参数检验方法，用于在随机删失数据的双样本情况下检验量表参数。随机删减数据大多出现在临床研究中，其中一些个体经历了感兴趣的事件（死亡）；一些个体退出或失去随访，还有一些个体在研究结束时仍然存活。通过与现有的一些检验方法在渐近相对效率方面的比较，对该检验方法的性能进行了研究。计算了检验所需的临界值。通过不同样本量和不同删减百分比的模拟研究，评估了检验的统计功率。通过将检验应用于现实生活中的数据集，说明了检验的工作原理。

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 Nonparametric Test for Randomly Censored Data

A nonparametric test for the testing of scale parameters, is proposed in two-sample situation with random censored data. Random censored data are mostly encountered in clinical studies, where some individuals experience the event of interest (death); some are drop-outs or loss to follow-ups and some are still alive at the end of study. The performance of test is studied by comparing it with some existing tests in terms of asymptotic relative efficiency. Critical values required for the test are computed. Statistical power of the test is assessed through simulation study with varying sample sizes and varying censoring percentages. The working of test is illustrated by applying it to a real-life data set.

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来源期刊

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

期刊介绍： Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.