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.
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.
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.
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.
Jönköping International Business School, February 16-17, 2012
Welcome to a workshop on high-dimensional analysis, in particular if you are a research student or are supervising some with an interest in high-dimensional problems. The main idea is that we should learn who is doing what in this very interesting area. We interpret high-dimensional analysis in a very broad sense.
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.