Modern Stochastics: Theory and Applications

Aims & Scope

Modern Stochastics: Theory and Applications publishes original research papers of highest quality in modern stochastics including: probability theory; mathematical statistics; theory of stochastic processes and random fields; stochastic analysis and stochastic differential equations; probabilistic aspects of fractal analysis; stochastic geometry; and various applied fields such as: financial mathematics; actuarial mathematics and risk theory; applications in economics, biology, physics, engineering; optimization and control.

With broad coverage of probability and statistics topics, we welcome original papers to present the deepest and highly innovative results, new tools, ideas and methods with rigorous mathematical background, and with a great potential for practical applications.

Journal will accept only papers of sufficiently high quality both in terms of scientific contents and the presentation of the results (including the graphical aspect of the work).

The journal welcomes articles of interdisciplinary nature.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Decision Sciences and Mathematics, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Plagiarism url
• Preservation url
• Apc url
• License

Plagiarism Policy

This journal follows a plagiarism policy. All submitted manuscripts are screened using reliable plagiarism detection software to ensure originality and academic integrity. Authors are responsible for proper citation and acknowledgment of all sources, and any form of plagiarism, including self-plagiarism, will not be tolerated.

For more details, please refer to our official: Plagiarism Policy.

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Decision Sciences and Mathematics, ensuring accessibility and quality in research dissemination.

This journal does not charge a mandatory Article Processing Charge (APC). However, optional open access publication may incur fees based on the publisher’s policies.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Fractional Cox–Ingersoll–Ross process with non-zero «mean»

  Citation: 21

  Authors: Yuliya, Anton

• Weighted entropy: basic inequalities

  Citation: 16

  Authors: Mark, Izabella, Yuri

• Quantifying non-monotonicity of functions and the lack of positivity in signed measures

  Citation: 15

  Authors: Youri, Ričardas

• On a bound of the absolute constant in the Berry–Esseen inequality for i.i.d. Bernoulli random variables

  Citation: 14

  Authors: Anatolii, Sergei, Vladimir

• Double barrier reflected BSDEs with stochastic Lipschitz coefficient

  Citation: 13

  Authors: Mohamed, Mohamed

• Construction of maximum likelihood estimator in the mixed fractional–fractional Brownian motion model with double long-range dependence

  Citation: 12

  Authors: Yuliya, Ivan

• Detecting independence of random vectors: generalized distance covariance and Gaussian covariance

  Citation: 11

  Authors: Björn, Martin, René L.

• Estimation of the drift parameter for the fractional stochastic heat equation via power variation

  Citation: 11

  Authors: Zeina, Ciprian

• Large deviations for drift parameter estimator of mixed fractional Ornstein–Uhlenbeck process

  Citation: 11

  Authors: Dmytro

• Generalized fractional Brownian motion

  Citation: 10

  Authors: Mounir