Friedrich Leisch
University of Natural Resources and Life Sciences, Vienna
230 Papers
917 Citations
Friedrich Leisch is an academic researcher from University of Natural Resources and Life Sciences, Vienna. The author has contributed to research in topics: Market segmentation & Computer science. The author has an hindex of 49, co-authored 219 publications. Previous affiliations of Friedrich Leisch include Vienna University of Technology & University of Erlangen-Nuremberg.
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Papers
Step 9: Customising the Marketing Mix
Sara Dolnicar,Bettina Grün,Friedrich Leisch +2 more
- 01 Jan 2018
TL;DR: In this article, the authors discuss strategic marketing areas that need to be integrated with the target segment decision (positioning and competition), and the tactical marketing decisions that follow from all of those strategic decisions in relation to product development and modification, pricing, distribution channel choice, and advertising and promotion.
Monitoring structural changes with the generalized fluctuation test
TL;DR: In this paper, generalized fluctuation tests based on the maximum and range of the fluctuation of moving estimates are proposed for monitoring structural changes and established a result characterizing the limiting behavior of this class of tests.
Interaction between serotonin 5-HT2A receptor gene and dopamine transporter (DAT1) gene polymorphisms influences personality trait of persistence in Austrian Caucasians
Alexandra Schosser,Karoline Fuchs,Theresa Scharl,Monika Schloegelhofer,Jochen Kindler,Nilufar Mossaheb,R. M. Kaufmann,Friedrich Leisch,Siegfried Kasper,Werner Sieghart,Harald N. Aschauer +10 more
TL;DR: It is concluded that an interaction between DAT1 and 5HT2A genes might influence the temperamental personality trait persistence.
Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis
Friedrich Leisch
- 01 Jan 2002
TL;DR: Sweave combines typesetting with LATEX and data anlysis with S into integrated statistical documents that can be automatically updated if data or analysis change, which allows truly reproducible research.
Music and timbre segmentation by recursive constrained K-means clustering
TL;DR: This work proposes to use order constrained solutions in K-means clustering to stabilize the results and improve the interpretability of the clustering, and to improve the misclassification error in the instrument recognition task.