Contact
Claudia von Brömssen Senior Lecturer at the Department of Energy and Technology; Applied statistics and mathematics
Telephone: 018-671720
E-mail: claudia.von.bromssen@slu.se
Seminar series and workshops
Last changed: 03 September 2024
Statistics@SLU gives seminar series and workshops on statistical modelling topics.
If you have suggestions for forthcoming topics contact a statistician at your campus or the Centre (statistics@slu.se).
2024 - Questionnaire surveys, advanced regression, GWR and GLMM
Seminar serie:
April 29, Annica de Groote and Peter Lundquist: Design of Questionnaire Surveys.
A survey can be looked upon as a process or series of survey operations. The design of each operation has the potential to affect the total quality of the survey results. Different methods of the way individuals are selected to the study have different strengths and weaknesses, and the choice can have great significance for the quality and credibility of the survey results. Another design choice of interest here is the strategy for dealing with nonresponse - both preventively and when nonresponse has arisen. Nonresponse is large in many surveys and can seriously bias the results.
May 17, Claudia von Brömssen: Geographically weighted regression model – identifying spatially differentiated relationships and trends.
Geographically Weighted Regression (GWR) is a spatial statistical technique to model spatially varying relationships between variables.
May 23-24, Reza Belaghi: Advanced Regression Analysis in Natural Sciences with R Software.
Through hands-on exercises and real-world examples, this workshop aims to equip attendees with a comprehensive skill set in advanced regression analysis, providing practical insights into modeling techniques for count data, survival analysis, and addressing issues related to excess zeros in the data.
June 12-13, Lars Rönnegård:Generalized Linear Mixed Models with extensions using R.
Generalized linear mixed models are GLMs with random effects. This is a class of models allowing non-normal outcomes and dependencies between observations with applications in analysis of repeated observations, spatial data and genetics.
2023 - Machine Learning
Introduction to Machine Learning in Natural Science: Modeling and Applications
November 29-30, Reza Belaghi.
In this workshop, we will explore the application of state-of-the-art machine learning models in the field of natural science, using real-world examples and various data sets. Our goal is to equip participants with the necessary knowledge and skills to apply machine learning in their research and scientific papers, and applications (whenever is needed).
2023 - Analysing network data that are partially observed
Johan Koskinen is a Lecturer in Statistics at Stockholms universitet. His main research interests centre on statistical modelling and Bayesian inference for networks.
Network data may be represented as binary graphs, either directed or undirected, and have a long history of being used to model and describe interaction between people and other entities, with formal approaches dating back to at least the start of the twentieth century. For a graph, the potential tie between a pair of nodes is represented by a binary indicator variable that we may call a tie-variable. These tie-variables are indexed by the labels of the nodes and can be organised in a so-called node by node adjacency matrix. Since the entries of the adjacency matrix are cross-classified by both the row node, and the column node, the tie-variables are highly interdependent. Exponential (family) random graph models (ERGMs) constitute a class of log-linear models with natural parameters that have as statistics a subset of graph statistics derived out of principled dependence assumptions. Due to these dependencies, the ERGM for a network does not marginalise and subgraphs of the network do not follow models of the same form. Here we discuss inference approaches for the parameters of the ERGM when some tie-variables are missing. The treatment of missing data in ERGM also applies to cases where data are missing by design, for example when the network data have been obtained through a link-tracing designs, such as snowball sampling. We describe a Bayesian approach for estimation and provide examples of applications to networks of young men who have sex with men, rebels in the Democratic Republic of the Congo, as well as the use of the Bayesian estimation scheme for imputing initial conditions in the analysis of network panel data. The latter case is illustrated with an application to social support networks in bushfire-affected communities in Australia. The use of the proposed approach is contingent of a number of fairly heroic assumptions, some of which will be brought up for discussion.
2022 - Survey methodology
Seminar serie: Survey methodology
September 16, Anton Grafström, Department of Forest Resource Management: Spatially balanced sampling for environmental monitoring
September 23, Eros Quesada, Department of Aquatic Resources: Anomaly detection techniques applied to fishery data for the identification of possible misreporting
September 30, Petter Kjellander, Department of Ecology – Distance sampling in wildlife management
October 7, Peter Lundquist, Department of Energy and Technology: Jackknife variance estimation for a complex survey of land use
2021 - Environmental Statistics
Seminar serie: Environmental Statistics
August 27, Claudia von Brömssen, SLU: Statistical methods for evaluation of temporal trends in environmental data. Recorded seminar
September 10, James Weldon, SLU: Change, stability and atmospheric pollutant effects in European forest vegetation (Defense of doctoral thesis)
September 17, Xin Zhao, SLU: Design-based sampling methods for environmental monitoring (Defense of doctoral thesis)
September 24, Arne Pommerening, SLU: Individual-based tree modelling for remote sensing data.
October 1, Jesper Rydén, SLU: Modelling extreme values: problems and concepts
October 15, Martin Sköld, Naturhistoriska riksmuseet, Disentangling effort and density in non-invasive genetic sampling by volunteers, the case of the Swedish Brown Bear monitoring programme
October 22, Annica de Groote, SLU: An introduction to sampling for natural resources
October 29, Anders Grimvall, Havsmiljöinstitutet and Linköpings university: How far can the evaluation of monitoring data be automated?
2020 - The digital SLU
Seminar serie: The digital SLU
August 28: Dorota Anglart, SLU & DeLaval: Generalized additive model for dairy cow somatic cell count predictions using sensor data
September 4: Aakash Chawade, Dept of Plant Breeding, SLU: Challenges and opportunities for analysis of omics data
September 11: Mats Söderström & Kristin Piikki. Dept. of soil and environment: Spatial data for mapping of crop and soil characteristics: Digital soil mapping, modelling crop status from remote sensing data, data fusion, multi-scale modelling, sampling strategies, validation
September 18: Keni Ren, Umeå University: Zoom in on the precision livestock farming
September 25: Måns Thulin, consultant in statistics and AI: An introduction to statistical learning
October 2: Johanna Bergman, AI innovations of Sweden: AI Innovation of Sweden and SLU
October 9: Moudud Alam, Dalarna University: Monitoring reindeer activities in their natural environment
October 16, Johan Holmgren, Dept. for forest resource management, SLU: Forest remote sensing on the individual tree level
October 23: Bo Stenberg & Johanna Wetterlind, Dept. of soil and environment: Spectral data from proximal sensors for analysis of soil properties: Instruments, data collection, data preparation, modelling, validation
2020 - Machine Learning
Workshop in Machine Learning
The term machine learning hides a variety of different statistical methods. The basic aim of these methods is to recognize or disclose structures / patterns in data. What is often sold a bit like highly complex mathematics usually has simple ideas as a basis.
Some of the theoretical ideas behind machine learning will be presented. However, the focus is on the implementation of these methods in R and the interpretation of the results. In the first part of the workshop, we will deal with continuous response variables, whereas in the second part we will work with category data.
We would like to invite Ph.D. students and researchers to a two-part workshop in R machine learning given by Sven Adler. Part one on 4-5 november (13:00-16:00 and 9:00-12:00), part two on 14-15 december (13:00-16:00 and 9:00-12:00) in Umeå.
2016 - Generalised additive models (GAM)
Workshop in Generalised Additive Models
Presentations:
Claudia von Brömssen: Introduction to General Additive Models
Claudia von Brömssen: Generalised and mixed models in the GAM context
Michal Zmihorski: GAM in ornothological studies
Henrik Thurfjell: GAM in modelling bear populations
Sven Adler: GAM in species habitat modelling
Stefan Widgren: GAM for modelling the prevalence of infectious diseases
Mikael Franko: GAM for modelling excess mortality of infectious diseases
Valerio Bartolino: GAM in fish ecology
Willem Dekker: GAM for modelling the annual recruitment data of young eels
Jens Fölster: GAM for trend analysis in water quality data
Claudia von Brömssen: GAM for modelling time-varying relationships
Invitation to the 2016 workshop and instruction for registration (pdf)
2015 - Mixed models
Workshop in Mixed models
Presentations:
Ulf Olsson: Mixed Models (first day)
Lin Shi, Food Science: The application of mixed model in exploring time dependent postprandial metabolic changes - a randomized, cross-over study (second day)
Andrew Allen, Wildlife, Fish and Environmental Studies: Understanding intraspecific variation in movement patterns of moose: A multi-scale approach (second day)
Wiebke Neumann, Wildlife, Fish and Environmental Studies: Using mixed models to analyze autocorrelated data in nested design. – Examples from the analyses of moose GPS positions across species' latitudinal range (second day)
Johan Pihel, Landscape Architecture, Planning and Management: Mixed effect models in Eye tracking and visual assessment studies of forest landscapes (second day)
Johannes Forkman, Crop Production Ecology: Randomized block trials with spatial correlation (second day)
Exercises:
Forest data:
- Description (first example in file)
- Data as SAS file
- Data as text file
- R-script solution
Dogs data:
- Description (second example in file)
- Data as SAS file
- Data as text file
- R-script solution
Ladybirds data:
- Description (first example)
- Data as SAS file
- Data as text file
- R-script solution
Bayisa data:
- Description (second example)
- Data as SAS file
- Data as text file
2014 - Generalised linear models
Workshop in generalised linear models
Föreläsningar
Ulf Olsson: Generaliserade linjära modeller
Ulf Olsson/Jonas Oliva Palau: Pseudo-binomial data
Johannes Forkman: Overdispersion
Johannes Forkman: Overdispersion - code and references
Data sets for computer labs:
Exercise 1: Lichen data, logistic regression
Exercise 2 and 3: Butterfly data, Poisson regression
SAS solutions - both exercises
Exercise 4: Sawfly data, logistic mixed model
Exercise 5: Cod data, zero-inflated poisson regression
2013 - Mixed models
Workshop in Mixed Models
Presentations:
Ulf Olsson: Mixed Models (first day)
Jan-Eric Englund: Does it matter if you use Mixed Models? (second day)
Johannes Forkman: Randomised block trials with spatial korrelation (second day)
Exercises:
Forest data:
- Description (first example in file)
- Data as SAS file
- Data as text file
- SAS solution
Dogs data:
- Description (second example in file)
- Data as SAS file
- Data as text file
- SAS solution
Ladybirds data:
Bayisa data: