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Fundamentals of Mathematical Statistics
Last Updated: 2026-02-05 16:15:25
Abstract
In this course we study the basics of theoretical statistics. The course includes methods for designing estimators, confidenceintervals and tests, and various ways to evaluate the accuracy ofestimators, confidence intervals and tests. We consider optimality criteria such as admissibility and minimaxity, as well asBayesian criteria. We will also present the asymptotic point of view.
Objective
The aim of this course is to gain insight into the main statistical ideas and concepts. The course considers classical low-dimensional models, with pointers towards today's highly complex models.
Resources
Learning Materials (Links)
- Main link
- Link
General Information
- Language
- English
- Levels
- BSC , MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 180 minutes
- Aids
- No individual written aids.
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Fundamentals of Mathematical Statistics |
|
4 h weekly |
| exercise | Fundamentals of Mathematical Statistics |
|
1 h weekly |
Offered In
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Core Courses (For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 14 of the required 26 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.)
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Track: Signal Processing and Machine Learning (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
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Specialisation Courses (These specialisation courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the MSc EEIT.)
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Major Courses (A total of 42 CP must be achieved during the Master Programme. The individual study plan is subject to the tutor's approval.)
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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Mathematical Statistics (The two core courses Fundamentals of Mathematical Statistics (401-3621-00L) and Likelihood Inference (401-8623-00L) are similar in content. Therefore only one of them can be recognised towards the Master’s degree in the core course area «Mathematical Statistics».)
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