Sara van de Geer: Katalogdaten im Herbstsemester 2023

NameFrau Prof. em. Dr. Sara van de Geer
LehrgebietMathematik
Adresse
Seminar für Statistik (SfS)
ETH Zürich, HG GO 14.2
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telefon+41 44 632 22 52
E-Mailsara.vandegeer@stat.math.ethz.ch
URLhttp://stat.ethz.ch/~vsara
DepartementMathematik
BeziehungProfessorin emerita

NummerTitelECTSUmfangDozierende
401-3621-DRLFundamentals of Mathematical Statistics Information Belegung eingeschränkt - Details anzeigen
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 KP4V + 1US. van de Geer
KurzbeschreibungIn 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.
LernzielThe 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.
KompetenzenKompetenzen
Methodenspezifische KompetenzenAnalytische Kompetenzengeprüft
Problemlösunggeprüft
Persönliche KompetenzenKreatives Denkengeprüft
401-3621-00LFundamentals of Mathematical Statistics Information 9 KP4V + 1US. van de Geer
KurzbeschreibungIn 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.
LernzielThe 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.
KompetenzenKompetenzen
Fachspezifische KompetenzenKonzepte und Theoriengeprüft
Methodenspezifische KompetenzenAnalytische Kompetenzengeprüft
Problemlösunggeprüft
Persönliche KompetenzenKreatives Denkengeprüft