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Mathematics
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Stochastic Processes and Its Applications
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Stochastic Processes 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: 31 May 2025 | Viewed by 703

Special Issue Editors

Dr. Elvira Di Nardo

E-Mail Website
Guest Editor
Department of Mathematics, University of Torino, Via Carlo Alberto 10, 10123 Torino, Italy
Interests: stochastic processes; mathematical statistics; symbolic methods
Prof. Dr. Luis Alberiko Gil-Alana

E-Mail Website
Guest Editor
Faculty of Economics and ICS, University of Navarra, E-31080 Pamplona, Spain
Interests: econometrics; quantitative methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue entitled "Stochastic Processes and Its Applications" is to publish original research articles discussing the latest developments in the theory and applications of stochastic processes. Applications include the modeling and analysis of stochastic dynamic systems used in biology, economics, medicine, queuing theory, reliability theory, and statistical physics. Topics such as time series analysis are particularly welcome, either in the time or frequency domains, covering issues such as strong time dependence, duration models, long memory, and fractional integration and cointegration. This Special Issue may also incorporate theoretical articles as long as they report applications of the models investigated.

Dr. Elvira Di Nardo
Prof. Dr. Luis Alberiko Gil-Alana
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

  • stochastic processes and their applications
  • computational probability
  • time series analysis

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Published Papers (1 paper)

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Research

25 pages, 702 KiB  
Article
Clustering Empirical Bootstrap Distribution Functions Parametrized by Galton–Watson Branching Processes
by Lauri Varmann and Helena Mouriño
Mathematics 2024, 12(15), 2409; https://doi.org/10.3390/math12152409 - 2 Aug 2024
Viewed by 495
Abstract
The nonparametric bootstrap has been used in cluster analysis for various purposes. One of those purposes is to account for sampling variability. This can be achieved by obtaining a bootstrap approximation of the sampling distribution function of the estimator of interest and then [...] Read more.
The nonparametric bootstrap has been used in cluster analysis for various purposes. One of those purposes is to account for sampling variability. This can be achieved by obtaining a bootstrap approximation of the sampling distribution function of the estimator of interest and then clustering those distribution functions. Although the consistency of the nonparametric bootstrap in estimating transformations of the sample mean has been known for decades, little is known about how it carries over to clustering. Here, we investigated this problem with a simulation study. We considered single-linkage agglomerative hierarchical clustering and a three-type branching process for parametrized transformations of random vectors of relative frequencies of possible types of the index case of each process. In total, there were nine factors and 216 simulation scenarios in a fully-factorial design. The ability of the bootstrap-based clustering to recover the ground truth clusterings was quantified by the adjusted transfer distance between partitions. The results showed that in the best 18 scenarios, the average value of the distance was less than 20 percent of the maximum possible distance value. We noticed that the results most notably depended on the number of retained clusters, the distribution for sampling the prevalence of types, and the sample size appearing in the denominators of relative frequency types. The comparison of the bootstrap-based clustering results with so-called uninformed random partitioning results showed that in the vast majority of scenarios considered, the bootstrap-based approach led, on average, to remarkably lower classification errors than the random partitioning. Full article
(This article belongs to the Special Issue Stochastic Processes and Its Applications)
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