Recent Advances and Applications of Mathematical and Reliability Models

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 536

Special Issue Editors


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Guest Editor
Department of Computer Science and Statistics, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea
Interests: reliablity modeling; statistical infernces; software relibilaity; machine learning

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Guest Editor
Department of Mathematics and Big Data Science, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
Interests: censoring scheme; reliability; statistical inference; Bayesian inference

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of the journal Mathematics entitled “Recent Advances and Applications of Mathematical and Reliability Models”. The recent developments in mathematics and computer science have largely led to benefits in many industrial tasks in different fields. In order to solve various problems in the current industry, it is necessary to understand the phenomenon and abstract the laws that govern it through mathematical modeling, and in this process, reliability must be improved using mathematical and reliability models. This Special Issue invites high-quality and original research on mathematical and reliability models from a variety of recently accessible fields to solve practical challenges in related fields. The topics of interest include, but are not limited to, the following:

  • Applied computing;
  • Applied mathematics;
  • Machine learning;
  • Neural networks;
  • Deep learning;
  • Bayesian method;
  • Computational methods;
  • Censoring;
  • Statistical methods in reliability;
  • Reliability modeling and optimization;
  • Applied mathematics in reliability;
  • Mathematical modeling in engineering;
  • Field data analysis.

Dr. Kwang-yoon Song
Prof. Dr. Kyeongjun Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

23 pages, 1091 KiB  
Article
Inference for Parameters of Exponential Distribution under Combined Type II Progressive Hybrid Censoring Scheme
by Kyeongjun Lee
Mathematics 2024, 12(6), 820; https://doi.org/10.3390/math12060820 - 11 Mar 2024
Viewed by 383
Abstract
In recent years, various forms of progressive hybrid censoring schemes (PHCS) have gained significant traction in survival and reliability analysis studies due to their versatility. However, these PHCS variants are often characterized by complexity stemming from the multitude of parameters involved in their [...] Read more.
In recent years, various forms of progressive hybrid censoring schemes (PHCS) have gained significant traction in survival and reliability analysis studies due to their versatility. However, these PHCS variants are often characterized by complexity stemming from the multitude of parameters involved in their specification. Consequently, the primary objective of this paper is to propose a unified approach termed combined type II progressive hybrid censoring scheme (ComT2PHCS) capable of encompassing several existing PHCS variations. Our analysis focuses specifically on the exponential distribution (ExDist). Bayesian inference techniques are employed to estimate the parameters of the ExDist under the ComT2PHCS. Additionally, we conduct fundamental distributional analyses and likelihood inference procedures. We derive the conditional moment-generating function (CondMGF) of maximum likelihood estimator (MLE) for parameters of the ExDist under ComT2PHCS. Further, we use CondMGF for the distribution of MLE for parameters of ExDist under ComT2PHCS. Finally, we provide an illustrative example to elucidate the inference methods derived in this paper. Full article
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