Courses

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.

Course Data
University: 
Umeå
Offered: 
2012:2
Level: 
Master
PhD
Credits (ECTS): 
8
Type of schedule: 
Regular schedule

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. Welcome to the course!

Rolf Larsson

Course Data
University: 
Uppsala
Offered: 
2012:2
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Regular schedule

Categorical Data Analysis

 

This course will be run during week 39, 24 september - monday, 28 september 2012 at 10:00 to frieday 28 September 12:00 (full-time course) at the School of <?xml:namespace prefix = st1 ns = "urn:schemas-microsoft-com:office:smarttags" />Business and Economics, Linnaeu University, Växjö.

For more information please contact ghazi.shukur@lnu.se

Course Data
University: 
Linné
Offered: 
2012:2
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Travel friendly schedule

Multivariate Analysis

Course Data
University: 
Uppsala
Offered: 
2012:1
Level: 
PhD
Credits (ECTS): 
15
Type of schedule: 
Regular 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.
Course Data
University: 
Örebro
Offered: 
2012:1
Level: 
PhD
Credits (ECTS): 
3
Type of schedule: 
Travel friendly schedule

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, asymptotic
Course Data
University: 
Örebro
Offered: 
2012:1
Level: 
PhD
Credits (ECTS): 
12
Type of schedule: 
Travel friendly schedule

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

Course Data
University: 
Jönköping
Offered: 
2012:1
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Schedule to be decided

Wavelets for Time Series Analysis (May 2018)

Course Code: 3FNA023
ECTS credits: 5
Level Doctoral course
Course contents
The course will cover the following topics:
- an introduction to wavelet analysis
- the Discrete Wavelet Transform (DWT)
- the Maximal Overlap DWT (MODWT)
- the wavelet variance, covariance, correlation and cross correlation

Course Data
University: 
Linné
Offered: 
Not scheduled
Level: 
PhD
Credits (ECTS): 
5
Type of schedule: 
Travel friendly schedule

Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) is an increasingly popular inference paradigm in applications where traditional (Bayesian or frequentist) inference is difficult because the likelihood function is e.g. computationally costly to evaluate or unavailable in closed, tractab- le form. In the course, we will start by briefly studying traditional Bayesian inference and different kinds of simulation-based inference methods in general.

Course Data
University: 
Chalmers
Offered: 
Not scheduled
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Schedule to be decided

Econometric analysis 15 hp, winter/spring 2018

Course Data
University: 
Linné
Offered: 
Not scheduled
Level: 
PhD
Credits (ECTS): 
15
Type of schedule: 
Travel friendly schedule

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