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[ 951MATSASIV14 ] VL Advanced Statistical Inference
Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
4 ECTS M1 - Master's programme 1. year Statistics Werner Müller 2 hpw Johannes Kepler University Linz
Detailed information
Pre-requisites keine
Original study plan Master's programme Statistics and Data Science 2025W
Learning Outcomes
Competences
The courses VL and UE Advanced Statistical Inference form an inseparable didactic unit. The learning outcomes described below are achieved through the interaction of the two courses:

Students are able to mathematically justify and possibly improve methods of advanced statistical inference, particularly asymptotics.

Skills Knowledge
  • Knowing and understanding of the basic problems, concepts and procedures of advanced statistical inference, particularly asymptotics (k1,k2)
  • Classify the concepts in their historical and contemporary meaning (k1)
  • Derive mathematical facts and theorems (k3,k4,k5)
  • Use of the Mathematica program package (k3)
  • Convergence concepts
  • Generating a random sample
  • Asymptotic evaluation of point and interval estimators and statistical tests
  • Delta method, efficiency, bootstrap theory
  • Robustness
  • Decision theory
  • Copulas
Criteria for evaluation Exam
Methods Lecture
Language English
Study material Casella G.and Berger R.L. (2002). Statistical Inference.
Changing subject? No
Corresponding lecture in collaboration with 951MASTPRTV14: VL Probability Theory (4 ECTS) equivalent to
5MSMS2VO: VL Mathematische Statistik II (8 ECTS)
On-site course
Maximum number of participants 100
Assignment procedure Assignment according to priority