Advances in Computational Statistics, Design of Experiments, and Its Applications

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 598

Special Issue Editor


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Guest Editor
School of Science, RMIT University, Melbourne, VIC 3000, Australia
Interests: experimental designs; statistical modelling; factorial designs; optimal designs; computational methods

Special Issue Information

Dear Colleagues,

Computational statistics has significant applications in many research fields, and the design of experiments is a traditional and widespread method of experimentation. The combination of the design of experiments and computational statistics is fascinating. The purpose of this Special Issue is to provide a collection of high-quality manuscripts that focus on all aspects of theoretical research and novel applications of computational statistics and/or the design of experiments, especially in social sciences and engineering.

Topics of interest include but are not limited to the following: algorithms and computational methods, factorial designs DSDs, response surface methodology, orthogonal designs, Latin hypercube designs, space-filling designs, estimation methods, statistical modelling, optimal designs, and applications in social sciences and engineering.

Dr. Stella Stylianou
Guest Editor

Manuscript Submission Information

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Keywords

  • experimental designs
  • statistical modelling
  • factorial designs
  • optimal designs
  • computational methods

Published Papers (1 paper)

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Research

21 pages, 619 KiB  
Article
Group Doubly Coupled Designs
by Weiping Zhou, Shigui Huang and Min Li
Mathematics 2024, 12(9), 1352; https://doi.org/10.3390/math12091352 - 29 Apr 2024
Viewed by 375
Abstract
Doubly coupled designs (DCDs) have better space-filling properties between the qualitative and quantitative factors than marginally coupled designs (MCDs) which are suitable for computer experiments with both qualitative and quantitative factors. In this paper, we propose a new class of DCDs, called group [...] Read more.
Doubly coupled designs (DCDs) have better space-filling properties between the qualitative and quantitative factors than marginally coupled designs (MCDs) which are suitable for computer experiments with both qualitative and quantitative factors. In this paper, we propose a new class of DCDs, called group doubly coupled designs (GDCDs), and provide methods for constructing two forms of GDCDs, within-group doubly coupled designs and between-group doubly coupled designs. The proposed GDCDs can accommodate more qualitative factors than DCDs, when the subdesigns for the qualitative factors are symmetric. The subdesigns of qualitative factors are not asymmetric in the existing results on DCDs, and in this paper, we construct GDCDs with symmetric and asymmetric designs for the qualitative factors, respectively. Moreover, detailed comparisons with existing MCDs show that GDCDs have better space-filling properties between qualitative and quantitative factors. Finally, the methods are particularly easy to implement. Full article
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