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Mathematics
Special Issues
New Advance of Mathematical Economics
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New Advance of Mathematical Economics

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

Deadline for manuscript submissions: 20 July 2024 | Viewed by 1089

Special Issue Editors

Prof. Dr. Miltiadis Chalikias

E-Mail Website
Guest Editor
Department of Accounting and Finance, Univercity of West Attica, Aegaleo, Greece
Interests: mathematical statistics (design of experiments); applied statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitrios Kallivokas

E-Mail Website
Guest Editor
Department of Business Administrration, Univercity of West Attica, Aegaleo, Athens, Greece
Interests: statistics education; applied statistics

Special Issue Information

Dear Colleagues,

Mathematical modeling is a valuable tool in Business and Economics. This Special Issue aims to discuss new theoretical insights to the following sections: Mathematical modeling of forecasting and Statistical Analysis, Econometric Models, Financial Mathematics, and Optimization of economic functions. It is our pleasure to invite you to contribute to this Issue by submitting research articles that will be subject to peer-review aiming to contribute to the development of research.

Prof. Dr. Miltiadis Chalikias
Prof. Dr. Dimitrios Kallivokas
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

  • mathematical models for forecasting
  • statistical analysis for forecasting
  • econometric models
  • financial mathematics
  • optimization of economic functions

Published Papers (1 paper)

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Research

25 pages, 340 KiB  
Article
Neural Network Approximation for Time Splitting Random Functions
by George A. Anastassiou and Dimitra Kouloumpou
Mathematics 2023, 11(9), 2183; https://doi.org/10.3390/math11092183 - 5 May 2023
Viewed by 727
Abstract
In this article we present the multivariate approximation of time splitting random functions defined on a box or RN,N∈N, by neural network operators of quasi-interpolation type. We achieve these approximations by obtaining quantitative-type Jackson inequalities engaging the [...] Read more.
In this article we present the multivariate approximation of time splitting random functions defined on a box or RN,N∈N, by neural network operators of quasi-interpolation type. We achieve these approximations by obtaining quantitative-type Jackson inequalities engaging the multivariate modulus of continuity of a related random function or its partial high-order derivatives. We use density functions to define our operators. These derive from the logistic and hyperbolic tangent sigmoid activation functions. Our convergences are both point-wise and uniform. The engaged feed-forward neural networks possess one hidden layer. We finish the article with a great variety of applications. Full article
(This article belongs to the Special Issue New Advance of Mathematical Economics)
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