Advances in Statistical Methods with Applications

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 679

Special Issue Editors


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Guest Editor
Faculty of Business & Economics, United Arab Emirates University, Al Ain 17555, United Arab Emirates
Interests: statistical theory; decision theory; Bayesian analysis; reliability theory; regression modeling

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Guest Editor
Department of Statistics and Operations Research, Kuwait University, Al-Shadadiyya, Kuwait City 12037, Kuwait
Interests: statistical analysis; Bayesian; probability distributions; ordered data; biostatistical applications; reliability studies; intensive computation and simulation

Special Issue Information

Dear Colleagues,

This Special Issue on "Advances in Statistical Methods with Applications" aims to highlight recent advancements in statistical methods and their practical applications across various fields. Recognizing the vital role of statistics in modern research, this Special Issue brings together recent investigations, methodologies, and case studies to explore novel statistical approaches and their implementation.

The articles featured in this Special Issue cover a wide range of topics, including, but not limited to, the following:

  1. Bayesian statistics and its applications;
  2. Machine learning techniques in statistical modeling;
  3. Multivariate analysis and its applications;
  4. Time series analysis and forecasting;
  5. Experimental design and optimization methods;
  6. Spatial statistics and spatial modeling;
  7. Regression analysis and modeling;
  8. Big data analytics and statistical inference;
  9. Decision theory and its applications;
  10. Reliability theory and its applications;
  11. Entropy and information studies.

Prof. Dr. Mohamed T. Madi
Prof. Dr. Mohammad Z. Raqab
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.

Keywords

  • big data and analysis
  • machine learning
  • mathematical modelling
  • stochastic modeling
  • Monte Carlo simulations
  • measures of divergence and entropy
  • optimization models
  • Bayesian analysis
  • biostatistical applications
  • entropy and information measures

Published Papers (1 paper)

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Research

13 pages, 290 KiB  
Article
Attribute Sampling Plan for Submitted Lots Based on Prior Information and Bayesian Approach
by Jing Zhao, Fengyun Zhang, Xuan Zhang, Yuping Hu and Wenxing Ding
Mathematics 2024, 12(11), 1692; https://doi.org/10.3390/math12111692 - 29 May 2024
Viewed by 294
Abstract
An acceptance sampling plan is a method used to make a decision about acceptance or rejection of a product based on adherence to a standard. Meanwhile, prior information, such as the process capability index (PCI), has been applied in different manufacturing industries to [...] Read more.
An acceptance sampling plan is a method used to make a decision about acceptance or rejection of a product based on adherence to a standard. Meanwhile, prior information, such as the process capability index (PCI), has been applied in different manufacturing industries to improve the quality of manufacturing processes and the quality inspection of products. In this paper, an attribute sampling plan is developed for submitted lots based on prior information and Bayesian approach. The new attribute sampling plans adjust sample sizes to prior information based on the status of the inspection target. To be specific, the sampling plans in this paper are indexed by the parameter trust with levels of low, medium, and high, where increasing trust level reduces sample size or risk. PCIs are an important basis for the choice of the trust level. In addition, multiple comparisons have been performed, including producer’s risk and consumer’s risk under different prior information parameters and different sample sizes. Full article
(This article belongs to the Special Issue Advances in Statistical Methods with Applications)
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