Course STAT210
Theory of Statistical Inference
Course offered :
- Current semester
- Next semester
Current programmes of study
Course offered by
| Number of credits | 10 |
| Course offered (semester) | Spring |
| Schedule | Schedule |
| Reading list | Reading list |
Language of Instruction
English
Pre-requirements
None
Learning Outcomes
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
of moments. - Be able to handle a parametric hypothesis testing problem and to use the likelihood ratio method.
- Have some knowledge of asymptotic statistics.
Contact Information
advice@math.uib.no
Course offered (semester)
Spring
Language of Instruction
English
Aim and Content
The course will give the conceptual and mathematical basis for further studies of statistical methods at a theoretic level.
Learning Outcomes
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
of moments. - Be able to handle a parametric hypothesis testing problem and to use the likelihood ratio method.
- Have some knowledge of asymptotic statistics.
Pre-requirements
None
Recommended previous knowledge
MAT112 Calculus II, MAT121 Linear Algebra, and STAT111 Statistical Methods
Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.
Contact Information
advice@math.uib.no