Theory of Statistical Inference

Teaching semester

Spring

Objectives 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.

Required Previous Knowledge

None

Recommended Previous Knowledge

MAT112 Calculus II, MAT121 Linear Algebra, and STAT111 Statistical Methods

Compulsory Assignments and Attendance

Compulsory excercises

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.

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.