Topics in Time Series Analysis: Old to New

During the fall 2019 professor Richard Davis, Columbia University, will give a course on Topics in Time Series Analysis: Old to New.

The course is aimed at advanced masters students and PhD students from Chalmers and Gothenburg University, and also welcomes students from other Scandinavian universities. The first meeting will be 

Monday, September 23, 13:15-15:00, room MVH 12 in the Mathematics Building, Chalmers tvärgata 3

Course Data
University: 
Chalmers
Offered: 
2019:2
Level: 
Master
PhD
Credits (ECTS): 
5
Type of schedule: 
Schedule to be decided

Topics in philosophy of science and statistical inference

The Department of Statistics at Uppsala University plans to give an introductory PhD course in philosophy of science in May-June and August-September 2019, see attached file.

Course Data
University: 
Uppsala
Offered: 
2019:1
Level: 
PhD
Credits (ECTS): 
5
Type of schedule: 
Travel friendly schedule

Some asymptotic methods in statistical inference

The aim is to provide some theory concerning asymptotic methods of statistics and probability with applications to inference problems. Knowledge of measure theory is not needed. Basic convergence concepts and results (with proofs) such as the Lindeberg-Lévy central limit theorem should have been covered in previous courses.
To sign up for the course send an e-mail to per.gosta.andersson@stat.su.se

 

Course Data
University: 
Stockholm
Offered: 
2019:1
Level: 
PhD
Credits (ECTS): 
8
Type of schedule: 
Travel friendly schedule

Causal inference

 

Course Data
University: 
Umeå
Offered: 
2018:2
Level: 
Master
PhD
Credits (ECTS): 
8
Type of schedule: 
Regular schedule

Ph.D. course in Statistical Inference HT2018-VT2019

Ph.D. course in Statistical Inference (15 hp) for students in
Statistics, Mathematical Statistics, and related areas.

Course Data
University: 
Linné
Offered: 
2018:2
Level: 
PhD
Credits (ECTS): 
15
Type of schedule: 
Travel friendly schedule

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

Advanced Machine Learning

The course covers some advanced models in machine learning. 
The models are analyzed mainly from a Bayesian perspective. 
The course is also a master's course on the LiU programme 'Statistics and Machine Learning'.

The course is organized into four topics:
Graphical Models
Hidden Markov Models
Gaussian Process Regression and Classification
State-space models

Each topic includes lectures, a computer lab and a follow-up seminar.

Course Data
University: 
Linköping
Offered: 
2017:2
Level: 
Master
PhD
Credits (ECTS): 
6
Type of schedule: 
Regular schedule

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

Financial Econometrics

The course offers an introduction to financial econometrics for second-cycle studies. It
covers the main parts of the spectrum of quantitative financial economics, discusses
important results in the empirical finance literature, and provides a comprehensive
knowledge to do empirical work in financial practice.
A student who has taken the course should:
- have a solid knowledge about basic themes in financial econometrics;
- know and be able to use concepts and notation that is frequently used in financial
econometrics;

Course Data
University: 
Uppsala
Offered: 
2016:2
Level: 
Master
PhD
Credits (ECTS): 
8
Type of schedule: 
Regular schedule

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