We are happy to invite to the course on Longitudinal Data Analysis (3hp). The course is funded by GRAPES  but all interested in Longitudinal Data Analysis can participate. The course will present material on Repeated Measurements, Mixed Linear Models, Generalized Linear Models, Model Validation and Data Analysis. All together the course comprises 12h lectures and 12h computer classes.

GRAPES will refund travel and accomodation costs for PhD students at Swedish universities.

In conjunction with the LinStat 2014 a mini-course (8h) entitled 'Survey sampling and linear models' will be held by professor Stephen Haslett, Massey University, New Zealand and professor Simo Puntanen, University of Tampere, Finland, August 23-24.

The course is sponsored by GRAPES and GRAPES will refund travel and accommodation costs for PhD students at Swedish universities.

This masters course introduces basic concepts, ideas and methods of Bayesian inference. It is given in the late fall at Stockholm University as part of the masters programme and in the late spring at Linköping University (then under the name Bayesian Learning) as part of the master's course in the programme 'Statistics and Machine Learning'. The focus is on models and methods in the field of machine learning, but most of the course content is of general statistical interest.
The course will be in English.

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.

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. Welcome to the course!

Rolf Larsson

Course content

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, asymptotic