Significant Applications in Economics, Business, Management and Industrial Statistics

Dear Colleagues,

Computational statistics and machine learning methodologies play an essential role in economics, business, management, and industry. Numerous researchers and technicians have dedicated their time to inventing novel computational statistics methodologies and machine learning algorithms to deal with data in various fields, such as engineering, reliability, economics, business, management, and surveys. The main purpose of this Special Issue of Mathematics is to provide a compendium of manuscripts that propose novel computational statistical methods for decision making, simulation studies, statistical inference, and relevant case studies. Topics of interest include, but are not limited to, the following:

• Supply chain management and logistics.
• Applications in economics, business, or management.
• Bayesian methods and their applications.
• Maintainability and availability.
• Machine learning and its applications.
• Modeling analysis and simulation.
• Optimization and simulation.
• Quality control and its applications.
• Reliability modeling and life testing.
• Risk assessment.

Prof. Dr. Tzong-Ru Tsai
Prof. Dr. Jianbao Chen
Prof. Dr. Yuhlong Lio
Guest Editors

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• supply chain
• logistics
• Bayesian estimation
• machine learning
• reliability analysis
• quality control
• preventive maintenance
• nowcasting
• dynamic factor models

Title: Parameter Estimation of Birnbaum-Saunders Distribution Under Competing Risks Using the Quantile Variant of the Expectation-Maximization Algorithm
Authors: Chanseok Park; Min Wang
Affiliation: Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, TX 78249, USA
Abstract: Competing risks models, also known as weakest-link models, are utilized to analyze diverse strength distributions exhibiting multi-modality, often attributed to various types of defects within the material. The weakest-link theory posits that a material’s fracture is dictated by its most severe defect. However, multimodal problems can become intricate due to potential censoring, a common constraint stemming from time and cost limitations during experiments. Additionally, determining the mode of failure can be challenging due to factors like the absence of suitable diagnostic tools, costly autopsy procedures, and other obstacles, collectively referred to as the masking problem. In this paper, we investigate the distribution of strength for multimodal failures with censored data. We consider both full and partial maskings and present an EM-type parameter estimate for the Birnbaum-Saunders distribution under competing risks. We compare the results with those obtained from other distributions such as lognormal, Weibull, and Wald (inverse-Gaussian) distributions. The effectiveness of the proposed method is demonstrated through two illustrative examples.