Symmetry in Computational Statistics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 1681

Special Issue Editor


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Guest Editor
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand
Interests: computational statistics; computational symbolic algebra; recreational mathematics; game theory

Special Issue Information

Dear Colleagues,

Dictionary definitions of symmetry emphasize the properties of systems that remain unaltered under certain operations and typically identify symmetry with a pleasing aesthetic.  Both of these aspects of symmetry are common in mathematical statistics and examples abound: symmetric probability distributions, symmetric or Hermitian variance matrices, idempotence of Fourier transforms, ergodic simulations, recursive functions, and so on.  Symmetry remains an important diagnostic for many systems and a valuable conceptual tool to clarify thinking. Symmetry may be exploited in a variety of ways to produce software that is faster, more elegant or efficient, more versatile, or easier to maintain.

This Special Edition of Symmetry is completely devoted to recent developments in statistics, especially computational mathematics and statistical software, in which symmetrical properties of systems are exploited or highlighted. We encourage relevant contributions from all fields related to computational statistics, as well as encourage authors to consider "statistics" in its broadest sense of manipulation of calculated quantities.

Submit your paper and select the Journal “Symmetry” and the Special Issue “Symmetry in Computational Statistics” via: MDPI submission system. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Dr. Robin Hankin
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. Symmetry is an international peer-reviewed open access monthly 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 2400 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

  • symmetry
  • computational statistics
  • computational software
  • mathematical statistics
  • computational symbolic manipulation

Published Papers (1 paper)

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Research

17 pages, 361 KiB  
Article
Generalization of Two-Sided Length Biased Inverse Gaussian Distributions and Applications
by Teerawat Simmachan and Wikanda Phaphan
Symmetry 2022, 14(10), 1965; https://doi.org/10.3390/sym14101965 - 20 Sep 2022
Cited by 1 | Viewed by 1319
Abstract
The notion of length-biased distribution can be used to develop adequate models. Length-biased distribution was known as a special case of weighted distribution. In this work, a new class of length-biased distribution, namely the two-sided length-biased inverse Gaussian distribution (TS-LBIG), was introduced. The [...] Read more.
The notion of length-biased distribution can be used to develop adequate models. Length-biased distribution was known as a special case of weighted distribution. In this work, a new class of length-biased distribution, namely the two-sided length-biased inverse Gaussian distribution (TS-LBIG), was introduced. The physical phenomenon of this scenario was described in a case of cracks developing from two sides. Since the probability density function of the original TS-LBIG distribution cannot be written in a closed-form expression, its generalization form was further introduced. Important properties such as the moment-generating function and survival function cannot be provided. We offered a different approach to solving this problem. Some distributional properties were investigated. The parameters were estimated by the method of the moment. Monte Carlo simulation studies were carried out to appraise the performance of the suggested estimators using bias, variance, and mean square error. An application of a real dataset was presented for illustration. The results showed that the suggested estimators performed better than the original study. The proposed distribution provided a more appropriate model than other candidate distributions for fitting based on Akaike information criterion. Full article
(This article belongs to the Special Issue Symmetry in Computational Statistics)
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