MAT12 Mathematical Statistics

Spring 2021

Topics

- Stochastic variables. probability distributions, one- and multivariate

- Transformations of variables and convolutions; moments

- Special distributions: Normal, exponential, Gamma, Beta, t, F and chisquare.

- Modes of convergence: in probability, in distribution; law of large numbers and the central limit theorem

- Estimation: The maximum likelihood principle, consistency and efficiency

- Hypothesis testing: The likelihood ratio method

- Bayesian methods
Learning outcome

Knowledge

After completing the course, the student knows the theory foundation for the statistical methods studied at the compulsory courses in statistics.

Skills

After completing the course, the student can formulate a stochastic model and fit it to a data set. The student can also evaluate the uncertainty of the fitted model.

General compentence

After completing the course, the student is prepared for a faster progression in quantitative courses in later master studies.
Teaching

The teaching is given in the form of lectures, where both theory, examples and exercises is discussed. The parts of the lectures where theory is presented are filmed.
Recommended prerequisites

MAT10 Analyse og lineær algebra (Calculus and Linear Algebra)
Required prerequisites

MET040/MET2 - Statistikk for økonomer (Statistics for Economists)
Credit reduction due to overlap

Correspond to MAT013
Requirements for course approval

A compulsory assignment must be passed in order to attend the written exam.
Assessment

5 hours written school exam.

UPDATED 12 October 2020:

In the autumn of 2020, the school exam will be converted to a: 5-hour individual home exam at the same time as the originally planned exam.

Grading Scale: A-F
Grading Scale

A-F
Computer tools

R
Literature

(HT) Hogg, Tanis and Zimmerman: Probability and statistical inference 9ed, Pearson, 2015 (8ed, Prentice Hall, 2010, by Hogg and Tanis is also possible to use)