Sara van de Geer: Catalogue data in Autumn Semester 2023 |
| Name | Prof. em. Dr. Sara van de Geer |
| Field | Mathematic |
| Address | Seminar für Statistik (SfS) ETH Zürich, HG GO 14.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 22 52 |
| sara.vandegeer@stat.math.ethz.ch | |
| URL | http://stat.ethz.ch/~vsara |
| Department | Mathematics |
| Relationship | Professor emerita |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 401-3621-DRL | Fundamentals of Mathematical Statistics   Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to info@zgsm.ch with their name, course number and student ID. Please see https://zgsm.math.uzh.ch/index.php?id=forum0 | 2 credits | 4V + 1U | S. van de Geer | ||||||||||||||
| Abstract | In this course we study the basics of theoretical statistics. The course includes methods for designing estimators, confidence intervals and tests, and various ways to evaluate the accuracy of estimators, confidence intervals and tests. We consider optimality criteria such as admissibility and minimaxity, as well as Bayesian criteria. We will also present the asymptotic point of view. | |||||||||||||||||
| Learning 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. | |||||||||||||||||
| Competencies |
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| 401-3621-00L | Fundamentals of Mathematical Statistics | 9 credits | 4V + 1U | S. van de Geer | ||||||||||||||
| Abstract | In this course we study the basics of theoretical statistics. The course includes methods for designing estimators, confidence intervals and tests, and various ways to evaluate the accuracy of estimators, confidence intervals and tests. We consider optimality criteria such as admissibility and minimaxity, as well as Bayesian criteria. We will also present the asymptotic point of view. | |||||||||||||||||
| Learning 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. | |||||||||||||||||
| Competencies |
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