Simulated Exact Confidence Intervals: With Applications to Censored Exponential Reliability Data

IF 1.3 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Applied Stochastic Models in Business and Industry Pub Date : 2024-10-26 DOI:10.1002/asmb.2900

Bo Henry Lindqvist, Gunnar Taraldsen

引用次数: 0

Abstract

A method for constructing exact simulated confidence intervals is presented, valid for situations with both discrete and continuous observations. The idea of the method is to invert a data generating function, which needs not be monotone, and where special attention is taken when the data generating function contains jumps. The method is applied to obtain exact confidence intervals for certain types of censored data from exponential distributions. The censoring schemes under study are earlier treated in the literature, and a comparison to these approaches is considered. The connection to fiducial inference is discussed, and a difference in the paradigm of obtaining intervals for the parameter is studied.

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模拟精确置信区间：与应用审查指数可靠性数据

提出了一种构造精确模拟置信区间的方法，该方法对离散和连续观测都有效。该方法的思想是反转一个数据生成函数，它不需要是单调的，并且当数据生成函数包含跳跃时需要特别注意。该方法用于从指数分布中获得某些类型截尾数据的精确置信区间。所研究的审查方案在先前的文献中已经讨论过，并考虑与这些方法进行比较。讨论了与基准推理的联系，并研究了参数区间获取范式的区别。

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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来源期刊

Applied Stochastic Models in Business and Industry 数学-数学跨学科应用

CiteScore

2.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.