Applied Statistics

Output

We summarize the group's main research output in form of presentations, workshops and posters (since 2018). The list here is representative of our work but by no means exhaustive.

For (peer-reviewed journal) publications with R. Furrer as (co-)author, see also publication summary, arXiv or ZORA. Software output is listed here.

2022

Lakatos, M., Lerch, S., Hemri, S., & Baran, S.: Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts. arXiv preprint, https://arxiv.org/pdf/2206.10237

Hemri, S., Bhend, J., Spirig, C., Nerini, D., Moret, L., Furrer, R., and Liniger, M. A.: Postprocessing of gridded precipitation forecasts using conditional generative adversarial networks and quantile regression, EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022, EGU22-5609,  https://doi.org/10.5194/egusphere-egu22-5609

Hemri, S., Dai, Y., Bhend, J., Spirig, C., Nerini, D., Furrer, R., Moret, L. and Liniger, M. A.: Spatially coherent postprocessing of cloud cover and precipitation forecasts using generative adversarial networks, ECMWF Machine Learning Workshop, online, 29 March - 1 April 2022,  Slides

 

2021

Hemri, S., Bhend, J., Spirig, C., Furrer, R., Moret, L., and Liniger, M. A.: Spatially consistent postprocessing of precipitation over complex topography , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-129, https://doi.org/10.5194/ems2021-129.

Furrer, R: Computational Reproduciblilty, DACH Epidemiologietagung, 1. - 3. September 2021, Bern, CH. Slides.

Furrer, R, Flury, R. and Blasi. F. Spatial Statistics for Huge Datasets and Best Practices, useR! 2021 Conference, online, https://www.youtube.com/watch?v=gQEM-cpLrmg.

Cherneva, K., Furrer, R. and Tarigan, B.: SampleSizeR: calculate sample sizes within completely randomized design, doi: 10.17605/OSF.IO/NF9P2 [online]. Available at: http://shiny.math.uzh.ch/git/reinhard.furrer/SampleSizeR/

Tarigan, B.: Planning Experiments and Sample Size Calculations, StatsBrief.

Tarigan, B. and Cherneva, K.: Sample Size Calculation for one-way ANOVA with Repeated Measures in Time, StatsBrief.

Tarigan, B.: Comparing survival curves, StatsBrief.

Dai, Y. and Hemri, S.: Spatially coherent postprocessing of cloud cover forecasts using generative adversarial networks, EGU General Assembly 2021, online, 19-30 Apr 2021, EGU21-4374, https://doi.org/10.5194/egusphere-egu21-4374

Brunner, M. I., Furrer, R., and Gilleland, E.: Functional data clustering as a powerful tool to group streamflow regimes and flood hydrographs, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-375, https://doi.org/10.5194/egusphere-egu21-375.

Dambon, J. A.: Gaussian Process-based Spatially Varying Coefficient Models, Kernel Club, Colorado School of Mines, Presentation. Slides.

2020

Furrer, R.: How to Peer Review. Presentation for the EpiBiostat PhD Program. Recorded presentation (uncut).

Dambon, J.: varycoef: Modeling Spatially Varying Coefficients. eRum 2020, virtual conference. Presentation. Slides.

Flury, R.: Identification of Dominant Features in Spatial Data. Research in progress talk. Slides.

Staudinger, M., Furrer, R. and Viviroli, D.: A framework to characterize flood events of defined return period ranges using functional boxplots, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU2020-9950, https://doi.org/10.5194/egusphere-egu2020-9950.

Dambon, J. A., Fahrländer, S. S., Karlen, S., Lehner, M., Schlesinger, J., Sigrist, F., Zimmermann, A.: Examining the Vintage Effect in Hedonic Pricing using Spatially Varying Coefficients Models: A Case Study of Single-Family Houses in the Canton of Zurich, Preprint, ResearchGate.

Dambon, J. A.: organization and chairing of invited topic-contributed session “Advances in Disease Mapping” at Joint Statistical Meetings 2020, virtual conference, Session Info.

2019

Dambon, J.: varycoef: An R Package to Model Spatially Varying Coefficients. Swiss Statistics Seminar, Bern, Switzerland. Poster.

Furrer, R.: Interpolation for huge spatial datasets: theory, implementations and ideas. University of Geneva, Presentation.

Flury, R.: A Spatial Field Decomposition Approach to Evaluate Biodiversity Indices on Dominant Scales. JSM Denver, USA, Presentation. Slides.

Dambon, J.: Spatially Varying Coefficients Models: How MLE Stacks up Against Other Methods. JSM Denver, USA, Presentation. Slides.

Furrer, R.: Scalable gapfilling in spatio- temporal remote sensing data. JSM Denver, USA, Presentation. Slides.

Flury, R.: Multiresolution Decomposition of Areal Count Data, Graspa 2019. Pescara, It. Poster.

Schuh, L.: Advancing Methods to Model Landscape Heterogeneity. Global Change and Biodiversity: Integrating the impact of earth and world drivers across scales, Monte Verità, Switzerland. Poster.

Dambon, J.: Spatially Varying Coefficients Models for Large Data using MLE. Spatial Statistics 2019, Sitges, S. Presentation.

Furrer, R.: Modeling and prediction of extreme events over networks. Workshop on "Causality and Extremes", Presentation. Slides.

Furrer, R.: Share your Code?. EBPI colloquium , Discussion. Slides.

Kratzer, G.: Regression models, mixed models and and introduction to ggplot: good scientific practice for Neuroscientists using R programming language. In: Advanced statistics workshop, University of Zurich, Feb. 6th. and 7th. Presentation in Workshop.

Furrer, R.: Interpolation for huge spatial datasets: theory, implementations and ideas. Department of Statistics, University of Uppsala, Sweden, Jan. 30th. Invited seminar talk. Slides.

2018

Kratzer, G.: Bayesian Networks meet Observational data. 1st Causality workshop, Zurich University, Switzerland, Dec. 4th. Workshop (Main Organizer). Slides.

Furrer, R.: The Power and Limits of Statistics. 2-day Molecular Life Sciences PhD Programme Course "Different perspectives on Responsible Research and Good Scientific Practice", Nov. 12th. Presentation. Slides.

Furrer, R.: Imputing missing values in satellite data: From parametric to non-parametric approaches. Institute for Geoinformatics, University of Münster, Nov. 13th. Invited seminar talk. Slides.

Kratzer, G.: Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology. Swiss Statistical Society, Bern University, Switzerland, Nov. 9th. 2018. Poster.

Dambon, J.: Spatially Varying Coefficients Models: A Comparison of Maximum Likelihood Estimators with other Estimators. Swiss Statistics Seminar 2018, University of Bern, Switzerland, Nov.9th. Poster.

Kratzer, G.: Multivariable analysis: variable and model selection in system epidemiology. Danone, Utrecht, the Netherlands, Oct. 4th. 2018. Invited seminar talk. Slides.

Furrer, R.: Imputing missing values in satellite data: From parametric to non-parametric approaches. Department of Statistics and Operations Research, Public University of Navarre, Spain, Sept. 26th. Invited seminar talk. Slides.

Kratzer, G.: Academical statistical consulting service for veterinary research and case study about ethic and statistics. M-14E Current topics of Laboratory Animal Science, University of Zurich, Sep. 14th. Presentation in Workshop.

Wang, C.: Generalised Spatial Fusion Model Framework for Multivariate Analysis of Point and Areal Data. GeoEnv2018, Belfast, July 4th. Presentation. Slides.

Kratzer, G.: Comparison between Suitable Priors for Additive Bayesian Networks. BAYSM 2018, University of Warwick, United Kingdom, Jul. 2nd. 2018. Poster.

Furrer, R.: Predicting missing values in spatio-temporal satellite data. ISNPS 2018, Salerno, June 14th. Presentation in Invited Session. Slides.

Kratzer, G.: Bayesian Networks Learning in a Nutshell. Brown Bag Seminar at ZHAW, Winterthur, Switzerland, May 30th. Invited seminar talk. Slides.

Kratzer, G.: Advances in Additive Bayesian Network applied to observational systems epidemiology datasets. Institute of Global Health, Geneva University, Switzerland, May 2nd. Invited seminar talk. Slides.

Schuh, L.: Regional climate regulation by different land cover types: comparing homogeneous and heterogeneous structures in agricultural landscapes. EGU, Vienna. Poster.

Kratzer, G.: Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology. Society for Veterinary Epidemiology and Preventive Medicine (SVEPM), Tallinn, Estonia, Mar. 22th. 2018. Poster.