TILM3608 Asymptotical Methods in Statistics (6 cr)

Cooperation network course

Network: Cross-institutional studies in advanced courses in mathematics and statistics

This course is offered through the Network for Advanced Studies in Mathematics. These studies are available for the following degree students:

  • Bachelor's Degree Programme in Mathematics
  • Master's Degree Programme in Mathematics
  • Bachelor's Degree Programme in Mathematics (Subject Teacher)
  • Master's Degree Programme in Mathematics (Subject Teacher)
  • Bachelor's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
  • Master's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
  • Doctoral Programme in Mathematics and Statistics
  • Doctoral Programme in Mathematics and Science (Specialication in Mathematics)

More about the network →

Open the course unit brochure on Sisu
Grading scale:
0-5

Description

This course serves as an introduction to the basic tools and concepts of asymptotic statistics. The course begins with careful study of various forms of convergence, and later these will be applied to point estimation, linear regression and the determination of confidence intervals, among others. One of the main objectives of the course is to deepen our understanding of the asymptotic results encountered on earlier statistics courses, where time constraints have prevented their closer study. Due to the nature of asymptotic results, also simulation (with the R-software) is one of the themes on the course.

Learning outcomes

After the course, the student • Is able to assess the effectiveness of statistical methods and estimators through their asymptotic properties. • Can derive limiting properties for several standard estimators. • Is able to interpret both the nature of asymptotic results and their relevance in practical situations.

Description of prerequisites

MATE5019 Calculus I (or equivalent skills on limits) TILM3561 Statistical Inference I TILM3707 Statistical Inference II TILM3588 Linear and Generalized Linear Models SMAT5306 Probability Theory, Advanced Course TILM3517 Basics of R-Language (or equivalent skills) + prerequisites for these courses Introduction to Computational Statistics is a recommended prerequisite. When this course is taken as a part of the Econometrics module, time series courses are recommended as prerequisites.