Prerequisite/Recommended prerequisite for participation in the module

The module builds on knowledge obtained by the module Statistisk inference for lineær modeller (Statistical Inference for Linear Models) as described in the Bsc curricula or similar courses.

Content, progress and pedagogy of the module

The students completing the module will have gained knowledge about a number of topics from the statistical sciences at an advanced level. The list may include, but is not limited to, the following topics:

Learning objectives

Knowledge

• state space models
• multivariate Gaussian distribution (and related distributions, e.g. Hotelling's T2 and Wishart distributions)
• generalised estimating equations
• dynamical linear models, including the Kalman filter
• population methods, specifically evolutionary computing and genetic algorithms
• meta analysis
• robust statistical methods including non-parametric models
• factor analysis

Skills

• can apply the relevant methodologies to one or more datasets by using appropriate software implementations, and interpret the output and modify the model parameters accordingly

• are able state the underlying assumptions and argue about limitations and extendibility of the methodology in one or more specific settings

• can assess goodness-of-fit for the models where appropriate

Competences

• can acquire supplementary knowledge about the relevant methodologies

• can combine appropriate topics from the course to analyse a specific dataset

• can in writing describe the methodologies, results and outcome from an analysis of a specific dataset

Type of instruction

Lectures with exercises.

Extent and expected workload

This is a 5 ECTS course module and the work load is expected to be 137,5 hours for the student.
 

Exam

Exams

Name of exam	Emner inden for statistisk videnskab
Type of exam	Written or oral exam
ECTS	5
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures