Recently published

Vcelar, S.; Jadhav, V.; Melcher, M., Auer, N.; Hrdina, A.; Sagmeister, R.; Heffner, K.; Puklowski, A.; Betenbaugh, M.; Wenger, T.; Leisch, F.; Baumann, M. and Borth, N. <br>
Karyotype variation of CHO host cell lines over time in culture characterized by chromosome counting and chromosome painting. Biotechnology and Bioengineering;2017. [DOI]

Puschenreiter M.; Gruber B.; Wenzel W. W.; Schindlegger Y.; Hann S.; Spangl B.; Schenkeveld W. D.C.; Kraemer S. M.; Oburger E.: Phytosiderophore-induced mobilization and uptake of Cd, Cu, Fe, Ni, Pb and Zn by wheat plants grown on metal-enriched soils. Environmental and Experimental Botany 138, 67-76, 2017. [DOI/ PDF]

Melcher, M.; Scharl, T.; Luchner, M.; Striedner, G. and Leisch, F.: Boosted Structured Additive Regression for Escherichia coli Fed-Batch Fermentation Modeling. Biotechnology and Bioengineering 114 (2), 321-334, 2017. [DOI]

Brockhaus S.; Melcher M.; Leisch F. and Greven S.: Boosting flexible functional regression models with a high number of functional historical effects. Stat Comput (2017) 27:913-926 [bib / DOI]

Parajka, J.; Blaschke, A. P.; Blöschl, G.; Haslinger, K.; Hepp, G.; Laaha, G.; Schöner, W.; Trautvetter, H.; Viglione, A. and Zessner, M: Uncertainty contributions to low-flow projections in Austria. Hydrology and Earth System Sciences, 20(5), 2085-2101, doi:10.5194/hess-20-2085-2016, 2016. [DOI]

Van Lanen H.A.J.; Laaha G.; Kingston, D.; Gauster T. et al Hydrology needed to manage droughts: the 2015 European case. Hydrological Processes, 2016. [DOI / PDF]

Melcher M.; Scharl T.; Spangl B.; Luchner M.; Cserjan M.; Bayer K.; Leisch F.; Striedner G. The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed-batch fermentations. Biotechnology Journal 10(11), 1770-1782, 2015. [bib / DOI]

Unbehaun W.; Uhlmann T.; Hössinger R.; Leisch F.; Gerike R. Women and men with care responsibilities in the European Alps - activity and mobility patterns of a diverse group. Mountain Research and Development, 34(3):276--290, 2014. [bib / DOI]

Van Loon A. F.; Laaha G. Hydrological drought severity explained by climate and catchment characteristics. Journal of Hydrology, doi:10.1016/j.jhydrol.2014.10.059, n.d. [DOI / PDF]

Haslinger K.; Koffler D.; Schöner W.; Laaha G. Exploring the link between meteorological drought and streamflow: Effects of climate-catchment interaction. Water Resources Research, 50(3), 2468–2487, doi:10.1002/2013WR015051, 2014. [DOI]

Laaha G.; Skøien J. O.; Blöschl G. Spatial prediction on river networks: comparison of top-kriging with regional regression. Hydrological Processes, 28(2), 315–324, doi:10.1002/hyp.9578, 2014. [DOI]

Skøien J. O.; Blöschl G.; Laaha G.; Pebesma E.; Parajka J.; Viglione A. rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks. Computers & Geosciences, 67, 180–190, doi:10.1016/j.cageo.2014.02.009, 2014. [DOI]

Szolgayova, E.; Laaha, G.; Blöschl, G.; Bucher, C. Factors influencing long range dependence in streamflow of European rivers: LONG RANGE DEPENDENCE IN RUNOFF OF EUROPEAN RIVERS. Hydrological Processes, 28(4), 1573–1586, doi:10.1002/hyp.9694, 2014. [DOI]

Nosrati K.; Laaha G.; Sharifnia S. A.; Rahimi M. Regional low flow analysis in Sefidrood Drainage Basin, Iran using principal component regression. Hydrology Research, doi:10.2166/nh.2014.087, n.d. [DOI / PDF]

Strohmeier S.; Laaha G.; Holzmann H.; Klik A. Magnitude and occurrence probability of soil loss: a risk analytical approach for the plot scale for two sites in Lower Austria. Land Degradation & Development, n.d. [DOI / PDF]

Dolnicar, S; Grun, B; Leisch, F; Schmidt, K. Required Sample Sizes for Data- Driven Market Segmentation Analyses in Tourism. Journal of Travel Research, 53(3):296--306, 2014. [bib / DOI]

Egger, B; Spangl, B; Koschier, EH. Habituation in Frankliniella occidentalis to deterrent plant compounds and their blend. ENTOMOL EXP APPL., 151(3): 231-238,2014. [DOI]

Eugster, MJA; Leisch, F; Strobl, C. (Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithm. Computational Statistics & Data Analysis, 71: 986-1000,2014. [bib / DOI / preprint]