ABSTRACT Balancing consumer's and producer's risk is an important consideration when planning tests. Instead of focusing on finding a single best test plan, we introduce a general framework to systematically identify a set of binomial test plans by leveraging the inverse relationship between the two risks. The framework is applied to compare a variety of assurance testing frameworks, including classical tests, and Bayesian reliability assurance tests such as the Bayesian assurance test, the assurance reliability demonstration test, and the coverage criterion test. Efficient algorithms are presented to compute the set of test plans, providing practitioners with a comprehensive range of options to choose from. In addition, we include a comparison to the sequential probability ratio test. We also provide formal proofs for the inverse relationship between consumer's and producer's risk in Bayesian reliability assurance tests that underlie our algorithms. A case study is presented to illustrate the framework's application and compare the risks associated with different test plans.

Hyoshin Kim

Alyson G. Wilson

Applied Stochastic Models in Business and Industry

ASMBI - APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY（前身为 APPLIED STOCHASTIC MODELS AND DATA ANALYSIS）于 1985 年首次出版，发表在随机建模、数据分析及其在商业、金融、保险、管理和生产中的应用之间的接口方面的贡献。 2007 年，ASMBI 成为国际商业和工业统计学会 (WWW.ISBIS.ORG) 的官方期刊。主要目标是发表技术性和实用性的论文，提出解决现实问题或具有巨大潜力的新成果。严谨的数学、创新的随机建模和合理的应用是要发表的论文的关键要素，经过非常有选择性的审查过程。该杂志对新想法非常开放，例如源自商业和行业问题或不确定性的数据科学和大数据工程量化，以及更传统的量化，如可靠性、质量控制、实验设计、管理流程、供应链和库存、保险、计量经济学、财务建模（前提是论文与实际问题相关）。该杂志还对解决商业和工业决策对环境、医疗保健和社会生活的影响的论文感兴趣。当与合理的应用程序和创新模型相结合时，最先进的计算方法也非常受欢迎。

Applied Stochastic Models in Business and Industry (formerly known as Applied Stochastic Models and Data Analysis) was first published in 1985 and focuses on contributions at the interface of stochastic modeling, data analysis, and their applications in business, finance, insurance, management, and production. In 2007, it became the official journal of the International Society for Business and Industrial Statistics (ISBIS). The primary aim is to publish technical and practical papers that propose solutions to real-world problems or present new findings with significant potential. Key elements for publication include rigorous mathematics, innovative stochastic modeling, and reasonable applications, all subjected to a highly selective review process. The journal is open to new ideas, particularly those arising from business and industry issues or quantifications in data science and big data engineering, as well as more traditional quantifications such as reliability, quality control, experimental design, management processes, supply chain and inventory management, insurance, econometrics, and financial modeling, provided they are relevant to real-world issues. The journal is also interested in papers that address the impact of business and industrial decisions on the environment, healthcare, and social life. State-of-the-art computational methods are also highly welcomed when combined with reasonable applications and innovative models.

《Applied Stochastic Models in Business and Industry》（前身为《Applied Stochastic Models and Data Analysis》）于1985年首次出版，致力于发表随机建模、数据分析及其在商业、金融、保险、管理和生产等领域应用的研究成果。2007年，该期刊成为国际商业和工业统计学会（ISBIS）的官方期刊。期刊的主要目标是发表技术性和实用性的论文，提出解决现实问题或具有巨大潜力的新成果。论文需具备严谨的数学基础、创新的随机建模和合理的应用，经过严格的审查过程。该期刊对新想法持开放态度，尤其是那些源自商业和行业问题的数据科学和大数据工程的量化研究，以及更传统的量化研究，如可靠性、质量控制、实验设计、管理流程、供应链和库存管理、保险、计量经济学和财务建模（前提是论文与实际问题相关）。此外，期刊也对探讨商业和工业决策对环境、医疗保健和社会生活影响的研究感兴趣。结合合理的应用和创新模型的先进计算方法也受到欢迎。

Category	Quartile
数学	4
数学, 数学跨学科应用	4
数学, 运筹学与管理科学	4
数学, 统计学与概率论	4

Comparing Risks for Binomial Reliability Assurance Test Planning

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Journal Info

Category	Quartile
STATISTICS & PROBABILITY	2