Objectives and Content
The course will give the conceptual and mathematical basis for further studies of statistical methods at a theoretic level.
After completed course, the students are expected to:
- Know the most common distributions and the exponential family.
- Be familiar with transformation of univariate and multivariate densities.
- Know the concept of covariance and conditional probability.
- Know the different notions of convergence i statistics like
convergence in probability, almost sure convergence and convergence in distribution.
- Be familiar with the concept of sufficiency and the likelihood principle.
- Know the most important estimation methods like maximum likelihood, least square and the method
- Be able to handle a parametric hypothesis testing problem and to use the likelihood ratio method.
- Have some knowledge of asymptotic statistics.
Required Previous Knowledge
Recommended Previous Knowledge
Forms of Assessment
Written examination, 5 hours. Examination support materials: Non- programmable calculator, according to model listed in faculty regulations.
Examination only in the spring.
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
For written exams, please note that the start time may change from 09:00 to 15:00 or vice versa until 14 days prior to the exam. The exam location will be published 14 days prior to the exam. Candidates must check their room allocation on Studentweb 3 days prior to the exam.
Type of assessment: Written examination
- 03.06.2020, 09:00
- 5 hours
- Withdrawal deadline