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About the Journal

The Austrian Journal of Statistics is an open-access journal (without any fees) including a long history. It is published approximately quarterly by the Austrian Statistical Society. Its general objective is to promote and extend the use of statistical methods in all kind of theoretical and applied disciplines. Special emphasis is on methods and results in official statistics. The Austrian Journal of Statistics is indexed in many data bases, such as Scopus (by Elsevier), Web of Science - ESCI by Clarivate Analytics (formely Thompson & Reuters), DOAJ, Scimago, and many more. 

 

Original papers and review articles in English will be published in the Austrian Journal of Statistics if judged consistently with these general aims. All papers will be refereed. Special topics sections will appear from time to time. Each section will have as a theme a specialized area of statistical application, theory, or methodology. Technical notes or problems for considerations under Shorter Communications are also invited. A special section is reserved for book reviews.

 

The current estimated impact factor (via Publish or Perish) is 0.775, see HERE, or even more indices HERE.

We are indexed in Scopus - the Austrian Journal of Statistics is indexed and listed in Scopus, DOAJ, Scimago and many other indices. Austrian Journal of Statistics ISNN number is 1026597X

Current Issue

Vol. 54 No. 3 (2025): Special Issue. In memorial: Fritz Leisch
					View Vol. 54 No. 3 (2025): Special Issue. In memorial: Fritz Leisch
This special issue of the Austrian Journal of Statistics is devoted to the memory of Friedrich "Fritz" Leisch who passed away after a serious illness a year ago, in April 2024. The idea for the issue was developed by a group of collaborators and friends of Fritz, consisting of Bettina Grün, Kurt Hornik, Torsten Hothorn, Theresa Scharl, and Achim Zeileis. Our aim was to compile contributions which honor Fritz' diverse scientific contributions to statistical computing, literate programming, cluster analysis and mixture models, statistical graphics, and applied statistics. Contributions were by invitation only and issued to a number of Fritz' co-authors. Bettina Grün and Theresa Scharl processed the special issue as Guest Editors. We would also like to thank the Editor of the Austria Journal of Statistics, Matthias Templ, and the Copy Editor, Klara Hruzova, for their support.

The special issue covers two contributions honoring Fritz' impact on reproducible research and literate programming by Roger Peng (University of Texas at Austin) and by Robert Gentleman (Dana Farber Cancer Institute), Antony Rossini (UCB and University of Washington), and Vincent Carey (Harvard Medical School), respectively. A contribution by Fritz Leisch and Torsten Hothorn (both at LMU Munich when drafting this in 2011) on inference for mixture models is finally published. In addition, Torsten Hothorn (University of Zurich) reflects on the reproducibility of the ten-year-old simulation study included in this work. Three contributions extend clustering methodology developed by Fritz and are accompanied by new R packages, available from the Comprehensive R Archive Network (CRAN). Dominik Ernst, Lena Ortega Menjivar, Theresa Scharl (all BOKU University), and Bettina Grün (WU Wien) discuss distance-based as well as model-based clustering methods for ordinal data; Matthias Medl, Ursula Laa (both BOKU University), and Dianne Cook (Monash University Melbourne) provide interactive exploration and visualization methods for market segmentation; and Lucas Sablica, Kurt Hornik, and Bettina Grün (all WU Wien) contribute to spherical and circular clustering in text mining. Fritz' general interest in different areas of statistics, in particular when useful for applied work, including robust, educational, and environmental statistics is reflected by the remaining contributions. Bernhard Spangl (BOKU University) investigates the robustification of the Kalman filter in a multivariate setting, Achim Zeileis (University of Innsbruck) presents different approaches for assessing measurement invariance and for detecting differential item functioning in the Rasch model along with their software implementation in R, and Gregor Laaha, Johannes Laimighofer, Nur Banu Özcelik (all BOKU University), and Svenja Fischer (Wageningen University) provide four case studies where accounting for heterogeneity based on domain knowledge improves the statistical modeling approach.

Bettina Grün and Theresa Scharl (Guest Editors)
Published: 2025-04-23
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