Courses

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, asymptoti
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

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.
Course Data
University: 
Umeå
Offered: 
2011:1
Level: 
PhD
Credits (ECTS): 
3
Type of schedule: 
Travel friendly schedule

Multi level analysis

COURSE SYLLABUS Multi level analysis

Course Data
University: 
Linné
Offered: 
2010:2
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Travel friendly schedule

Statistics for register based research

The course covers the following topics related to register based research:

- Registers, measurement and quality issues

-Causality and inference in observational studies

-Graphical models, multivariate analysis

-Registers, population based studies and surveys, combining different sources of data

Course Data
University: 
Umeå
Offered: 
2010:1
Level: 
Master
PhD
Credits (ECTS): 
8
Type of schedule: 
Travel friendly schedule

Multivariate Analysis


Course Data
University: 
Uppsala
Offered: 
2010:1
Level: 
PhD
Credits (ECTS): 
15
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
Course Data
University: 
Örebro
Offered: 
2009:2
Level: 
PhD
Credits (ECTS): 
12
Type of schedule: 
Travel friendly schedule

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