2009:2, 2010:1, 2010:2, 2011:1, 2011:2, Not scheduled, 2012:1, 2012:2, 2013:1, 2013:2
Causal inference in register studies
In this course we introduce causal inference with main focus on two approaches; i) the potential outcome framework/ Rubin Causal Model and ii) Causal inference with graphical models.
Identification of causal effects in the two approaches is studied. Non and semi-parametric estimators of causal effects commonly associated with the potential outcome framework will be presented. A general introduction to graphical models is also given.
Weeks 45-03, 50% tempo http://www.usbe.umu.se/english/dept/stat/education/courses/course/?code=...
Identification of causal effects in the two approaches is studied. Non and semi-parametric estimators of causal effects commonly associated with the potential outcome framework will be presented. A general introduction to graphical models is also given.
Weeks 45-03, 50% tempo http://www.usbe.umu.se/english/dept/stat/education/courses/course/?code=...
Bayesian Learning
The course introduces basic concepts, ideas and methods of Bayesian inference. It is coordinated with a second-year master's course in the programme 'Statistics and Data Mining', and the focus with be on models and methods in the field of data mining and machine learning, but most of the course content is of general statistical interest.
The course will be in English and will be given in the period October 30 - December 6, 2012.
Asymptotic Theory, Uppsala 2012
Starting November 5, I will give a course in asymptotic theory at the PhD level in Statistics. The course book is Ferguson, T.S. A
Course in Large Sample Theory, Chapman and Hall 1996. Lectures are at 10.15-14 on Mondays, ending December 17. Examination is through take home exercises. For further information, see the attached schedule.
Spatial Statistics (Winter Conference)
Course content
Introduction to spatial data analysis, representing space, interfacing geographical information systems, space-time data, spatial statistics overview (point patterns, geostatistics, areal data). Use of GIS and spatial statistics for radioecological modeling and mapping using Chernobyl- and Fukushima-related data, focusing on public health protection.Advanced Probability and Inference
Content
- Mathematical background (limits, series, order relations and rates of convergence, continuity, sets)
- Measure theoretic foundations of probability (probability triplets, random variables, independence, expected values, change of variable)
- Stochastic convergence (almost sure convergence, convergence in probability, convergence in distribution, laws of large numbers, central limit theorems, non-iid stochastic variables)
- Conditional probability and expectation
- Statistical tests (size and critical values, power, efficiency, asy
Multi Level modelling
The
aim of the course is to give the students basic knowledge in multilevel
modelling from both theoretical as well as practical side. The course is
designed to help doctoral students in their empirical analysis with multi-level
data. The course will provide an up-to-date overview on the most commonly used
Incomplete data: semi-parametric and Bayesian methods (Winter Conference)
Incomplete data is a common phenomenon in longitudinal studies based on surveys and/or population registers. In such studies individuals are followed through time and data may be incomplete due, e.g., to drop out/attrition (individuals intentionally leave the study, individuals leave the study because they move or die, etc.) and censoring (due to end of study, death, etc). Incomplete data may also arise due to selection mechanisms, for instance, in meta-analyses (publication bias) and causal inference in observational studies.
