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

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.