Advances in Statistical Simulation and Computing

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: 20 February 2025 | Viewed by 1800

Special Issue Editors


E-Mail Website
Guest Editor
Departamento de Enfermería, Facultad de Ciencias de la Salud y de los Alimentos, Universidad del Bío-Bío, Chillán 3800708, Chile
Interests: goodness of fit; applied statistics; time series; modeling

E-Mail Website
Guest Editor
Statistics Department, Faculty of Physical Sciences and Mathematics, Concepción University, Concepción 4030000, Chile
Interests: Bayesian statistic; time series; analysis and construction of models for space–time data; timely space patterns

Special Issue Information

Dear Colleagues,

Statistical simulation and computing are essential tools for addressing complex problems in various disciplines that would otherwise be intractable. On the one hand, there are simulation techniques based on sampling that are invaluable resources for exploring statistical and probalistic models. Moreover, there is the growing availability of computational potential and the development of sophisticated algorithms that allow speeding up the delivery time of results.

This Special Issue aims to gather articles that present theoretical and applied advancements in simulation methodologies, computational algorithms, and practical applications across various scientific and engineering disciplines. We seek contributions that address new statistical simulation techniques, improvements in existing algorithms, and case studies demonstrating the applicability and efficiency of these methods in real-world problems. We invite researchers and professionals to share their findings and contribute to the academic discussion on the future of statistical simulation and computing.

Dr. Francisco Novoa-Muñoz
Dr. Bernardo M. Lagos-Álvarez
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. Axioms 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

  • statistical simulation
  • computational statistics
  • algorithms and practical applications
  • theoretical advancements in statistical simulations
  • monte carlo method
  • simulation techniques
  • programing techniques (e.g., modular, dynamic, functional, concurrent, dynamic, etc.)
  • probabilistic programing
  • parallel programing applied to statistics
  • concurrent programing applied to statistics
  • distributed programing applied to statistics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

19 pages, 1058 KiB  
Article
Maximum Penalized Likelihood Estimation of the Skew–t Link Model for Binomial Response Data
by Omar Chocotea-Poca, Orietta Nicolis and Germán Ibacache-Pulgar
Axioms 2024, 13(11), 749; https://doi.org/10.3390/axioms13110749 - 30 Oct 2024
Viewed by 796
Abstract
A critical aspect of modeling binomial response data is selecting an appropriate link function, as an improper choice can significantly affect model precision. This paper introduces the skew–t link model, an extension of the skew–probit model, offering increased flexibility by incorporating both [...] Read more.
A critical aspect of modeling binomial response data is selecting an appropriate link function, as an improper choice can significantly affect model precision. This paper introduces the skew–t link model, an extension of the skew–probit model, offering increased flexibility by incorporating both asymmetry and heavy tails, making it suitable for asymmetric and complex data structures. A penalized likelihood-based estimation method is proposed to stabilize parameter estimation, particularly for the asymmetry parameter. Extensive simulation studies demonstrate the model’s superior performance in terms of lower bias, root mean squared error (RMSE), and robustness compared to traditional symmetric models like probit and logit. Furthermore, the model is applied to two real-world datasets: one concerning women’s labor participation and another related to cardiovascular disease outcomes, both showing superior fitting capabilities compared to more traditional models (with probit and the skew–probit links). These findings highlight the model’s applicability to socioeconomic and medical research, characterized by skew and asymmetric data. Moreover, the proposed model could be applied in various domains where data exhibit asymmetry and complex structures. Full article
(This article belongs to the Special Issue Advances in Statistical Simulation and Computing)
Show Figures

Figure 1

17 pages, 892 KiB  
Article
Bivariate Pareto–Feller Distribution Based on Appell Hypergeometric Function
by Christian Caamaño-Carrillo, Moreno Bevilacqua, Michael Zamudio-Monserratt and Javier E. Contreras-Reyes
Axioms 2024, 13(10), 701; https://doi.org/10.3390/axioms13100701 - 9 Oct 2024
Viewed by 649
Abstract
The Pareto–Feller distribution has been widely used across various disciplines to model “heavy-tailed” phenomena, where extreme events such as high incomes or large losses are of interest. In this paper, we present a new bivariate distribution based on the Appell hypergeometric function with [...] Read more.
The Pareto–Feller distribution has been widely used across various disciplines to model “heavy-tailed” phenomena, where extreme events such as high incomes or large losses are of interest. In this paper, we present a new bivariate distribution based on the Appell hypergeometric function with marginal Pareto–Feller distributions obtained from two independent gamma random variables. The proposed distribution has the beta prime marginal distributions as special case, which were obtained using a Kibble-type bivariate gamma distribution, and the stochastic representation was obtained by the quotient of a scale mixture of two gamma random variables. This result can be viewed as a generalization of the standard bivariate beta I (or inverted bivariate beta distribution). Moreover, the obtained bivariate density is based on two confluent hypergeometric functions. Then, we derive the probability distribution function, the cumulative distribution function, the moment-generating function, the characteristic function, the approximated differential entropy, and the approximated mutual information index. Based on numerical examples, the exact and approximated expressions are shown. Full article
(This article belongs to the Special Issue Advances in Statistical Simulation and Computing)
Show Figures

Figure 1

Back to TopTop