Submissions

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Submission Preparation Checklist

As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.
  • The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
  • The submission file is produced using the LaTeX file format provided by the journal (https://github.com/matthias-da/ajs-public)
  • All illustrations, figures, and tables are placed within the text at the appropriate points, rather than at the end. The figures are included in vector-graphics such as PDF and not as pixel-graphics such as JPG or PNG.

  • The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines, which is found in About the Journal.

    Title of the manuscript and references in title case style, section headers in case style.

  • Optinally, the names and adresses of at least three potential reviewers are submitted together with the manuscript.

Author Guidelines

The original LaTeX-file (including a tex and a bibtex file) should be used as a template for the setting up of a text to be submitted in computer readable form. Papers not considering our style might be automatically rejected. The guidelines and template can be downloaded at https://github.com/matthias-da/ajs-public . Additionally, they can be found on Overleaf, i.e. you can type your manuscript also online in Overleaf.  

Authors are strongly encouraged to produce their papers using LaTeX. Other formats are only accepted rarely in case you reject the benefits of LaTeX. If you really need to write your manuscript using Word then please do it without much formating so that we are able to translate it to LaTeX in an automatised manner. Manuscripts in Word must be exceptional in quality to be accepted for review.

Note that also reproducible text files based on Sweave, knitr, markdown or brew are accepted as long as the original LaTeX style is considered.

There is no submission fee, no processing fee, nor any publication fee.

Special Issue Department of Probability, Statistics and Actuarial Mathematics at TSNU of Kyiv

Before submitting to this special issue, please reach out to Ludmila Sakhno at [email protected] if you have not yet been in contact with her.

Special Issue. In memorial: Fritz Leisch

Submission with invitation only.

Special Issue on Compositional Data Analysis and CoDaWork 2024

Submissions to the special issue for compositional data analysis in general and also for CoDaWork 2024 contributions.

Special Issue iCMS 2025

Only for participants of the iCMS 2025 conference. 

Special Issue: New Directions in Statistical Modeling and Inference for Big Data Analytics

Description:
Large-scale data collection followed by the application of statistics and other data analysis
methods to find trends, patterns, and insights is recognized as statistical analysis. The act of
looking through vast and diverse data sets to find hidden patterns, correlations, and insights is
known as "big data analytics." Businesses can also use these insights to enhance operations,
make data-driven choices, and provide individualized consumer experiences. Big data can be
divided into three categories: unstructured, semi-structured, and structured. Traditional
databases can easily accommodate structured data since it is well-organized. While
unstructured data, like text or multimedia, lacks a predetermined structure, semi-structured
data, like JSON or XML, is somewhat organized. Raw data is transformed into useful
insights through data analytics. It encompasses a variety of instruments, methods, and
procedures for leveraging data to identify patterns and resolve issues. Data analytics has the
power to influence corporate procedures, enhance judgment, and promote company
expansion. Statistical modeling is the process of creating sample data and forecasting the real
world using mathematical models and statistical presumptions.
A set of probability distributions on a set of all potential experiment outcomes is called a
statistical model. The act of gathering, reviewing, and analyzing vast volumes of data in order
to identify patterns, insights, and market trends that might assist businesses in making better
decisions is known as big data analytics. Each has a distinct function and provides differing
degrees of understanding. When combined, they give companies the ability to fully
comprehend their big data and make decisions that will lead to better performance. Big data
enables real-time data collection, processing, and analysis so that one can swiftly adjust and
obtain a competitive edge. New features, updates, and products can be planned, produced,
and launched more quickly and efficiently with the help of these insights. Hadoop is a Java-
based open source framework that controls how much data is processed and stored for
applications. Hadoop handles big data and analytics tasks by dividing workloads into smaller
tasks that can be completed concurrently using distributed storage and parallel processing. It
takes time to transform huge data into a useful format. large data can be transformed into
large insights if it is prepared through advanced analytics procedures. Among these
techniques for analyzing big data are: By spotting irregularities and forming data clusters,
data mining combs through big information to find trends and connections. Professionals
utilize software applications, programs, and other tools known as data analysis tools to
examine data sets in ways that describe the information's overall context and yield useful
information for predictions, decision-making, and insightful analysis.
A collection of information obtained by research, analysis, measurements, or observations is
called data. They could include names, statistics, facts, figures, or even descriptions of
various objects. Data is arranged using tables, charts, or graphs. Statistical tools aid in the
cleansing and sorting of data. Through a variety of data cleaning methods, they also assist in

locating and resolving problems with the quality of the data. It's important to remember that
not all data may be pertinent while working with big data collections. A wide range of
contributions is encouraged, embracing various disciplines and viewpoints without
restriction: New Directions in Statistical Modeling and Inference for Big Data Analytics.
List of topics:
● Efficient statistical models for big data with high dimensions.
● Bayesian Inference Methods for Big Data Insights in Real Time.
● Big Data Uses Using Statistical Modelling for Data Streaming.
● Inference of Causation in Complicated Big Data Systems.
● Sturdy statistical techniques for incomplete and noisy big data.
● Developments in Large-Scale Dataset Hypothesis Testing.
● Methods of high-performance computing for Big Data Statistical Inference.
● Combining statistical modeling and machine learning for big data analytics.
● Quantification and Propagation of Uncertainty in Big Data Models.
● Semiparametric and Nonparametric Techniques for analyzing Large Data.
● Statistical Models for Big Data Applications in Space and Time.
● Personalized Analytics with Big Data using Adaptive Statistical Techniques.
● developments in mixed-effects and multivariate models for big data.
● Big Data Analytics: Ethical Aspects of Statistical Inference.

Important Dates:
Submissions Due: 05.09.2025
Preliminary Notification: 10.11.2025
Revisions Due: 15.01.2026
Final Notification: 20.03.2026
Publication date will be based on journal decision

Guest Editor Detailed Information:
Dr. Norshakirah Aziz
Associate Professor,
Department of Computer and Information Sciences,
Universiti Teknologi Petronas,
Seri Iskandar 32610, Malaysia
Research Link: https://scholar.google.com.my/citations?user=7ydYY4MAAAAJ
Email: [email protected], [email protected]

Dr. Hiroyuki Iida
Professor
Information Science, Human Information Science,
Japan Advanced Institute of Science and Technology,
Nomi, Ishikawa 923-1292 Japan
Research Link: https://scholar.google.com/citations?user=zVSgcLMAAAAJ
Email: [email protected]

Dr. Abdullahi Abubakar Imam
School of Digital Science,
Universiti Brunei Darussalam,
Gadong BE1410, Brunei
Research Link: https://scholar.google.com.my/citations?user=6XPY-XoAAAAJ
Email: [email protected]

Author Guidelines
-Authors can find the LaTeX sources
(Overleaf: https://www.overleaf.com/latex/templates/austrian-journal-of-statistics-
template/qskpxhjnqkbj or https://github.com/matthias-da/ajs-public) that it will be a special
issue for the Austrian Journal of Statistics ( www.ajs.or.at). 
- AJS is an open-access journal without fees with author-friendly
copyrights and that the Austrian Journal of Statistics is indexed and listed in Scopus, DOAJ,
Scimago and many other indices. 
- AJS reserves the right to reject papers or request further revisions after acceptance of the guest editors, if manuscripts do not meet the journal's quality standards. 
-There is no submission fee, no processing fee, nor any publication fee.

Copyright Notice

The Austrian Journal of Statistics publish open access articles under the terms of the Creative Commons Attribution (CC BY) License. 

The Creative Commons Attribution License (CC-BY) allows users to copy, distribute and transmit an article, adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed.

Copyright on any research article published by the Austrian Journal of Statistics is retained by the author(s). Authors grant the Austrian Journal of Statistics a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.

 

Manuscripts should be unpublished and not be under consideration for publication elsewhere. By submitting an article, the author(s) certify that the article is their original work, that they have the right to submit the article for publication, and that they can grant the above license.

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