An initial investigation for employing ACH depth function in degradation model selection: A case study with real data

IF 1.3 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Applied Stochastic Models in Business and Industry Pub Date : 2024-01-14 DOI:10.1002/asmb.2844

Arefe Asadi, Mitra Fouladirad, Diego Tomassi

引用次数: 0

Abstract

In degradation modeling, stochastic processes often do not meet the classical properties necessary for traditional goodness-of-fit tests. This paper presents an initial investigation into employing the ACH depth function and its potential in degradation model selection. We commence by presenting various stochastic processes as degradation models and their selection criteria. Subsequently, we delve into the ACH depth function, highlighting its potential in this context. Through simulated data, we assess the application of this functional depth measure for model selection. The methodology's validity is further reinforced by its application to real-world data, underscoring its effectiveness.

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在退化模型选择中使用 ACH 深度函数的初步研究：真实数据案例研究

在降解建模中，随机过程往往不符合传统拟合优度测试所需的经典特性。本文初步探讨了如何利用 ACH 深度函数及其在降解模型选择中的潜力。我们首先介绍了作为降解模型的各种随机过程及其选择标准。随后，我们深入研究了 ACH 深度函数，强调了它在这方面的潜力。通过模拟数据，我们评估了这种用于模型选择的函数深度测量的应用情况。该方法在实际数据中的应用进一步加强了其有效性。

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.