Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Statistik Master Information
Die hier aufgelisteten Lehrveranstaltungen gehören zum Curriculum des Master-Studiengangs Statistik. Die entsprechenden KP gelten nicht als Mobilitäts-KP, auch wenn gewisse Lerneinheiten nicht an der ETH Zürich belegt werden können.
Vertiefungs- und Wahlfächer
Statistische und mathematische Fächer
NummerTitelTypECTSUmfangDozierende
401-8618-00LStatistical Methods in Epidemiology (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: STA408

Beachten Sie die Einschreibungstermine an der UZH: Link
W5 KP3GUni-Dozierende
KurzbeschreibungAnalysis of case-control and cohort studies. The most relevant measures
of effect (odds and rate ratios) are introduced, and methods for
adjusting for confounders (Mantel-Haenszel, regression) are thoroughly
discussed. Advanced topics such as measurement error and propensity
score adjustments are also covered. We will outline statistical methods
for case-crossover and case series studies etc.
Lernziel
401-4626-00LAdvanced Statistical Modelling: Mixed ModelsW4 KP2VM. Mächler
KurzbeschreibungMixed Models = (*| generalized| non-) linear Mixed-effects Models, extend traditional regression models by adding "random effect" terms.

In applications, such models are called "hierarchical models", "repeated measures" or "split plot designs". Mixed models are widely used and appropriate in an aera of complex data measured from living creatures from biology to human sciences.
Lernziel- Becoming aware how mixed models are more realistic and more powerful in many cases than traditional ("fixed-effects only") regression models.

- Learning to fit such models to data correctly, critically interpreting results for such model fits, and hence learning to work the creative cycle of responsible statistical data analysis:
"fit -> interpret & diagnose -> modify the fit -> interpret & ...."

- Becoming aware of computational and methodological limitations of these models, even when using state-of-the art software.
InhaltThe lecture will build on various examples, use R and notably the `lme4` package, to illustrate concepts. The relevant R scripts are made available online.

Inference (significance of factors, confidence intervals) will focus on the more realistic *un*balanced situation where classical (ANOVA, sum of squares etc) methods are known to be deficient. Hence, Maximum Likelihood (ML) and its variant, "REML", will be used for estimation and inference.
SkriptWe will work with an unfinished book proposal from Prof Douglas Bates, Wisconsin, USA which itself is a mixture of theory and worked R code examples.

These lecture notes and all R scripts are made available from
Link
Literatur(see web page and lecture notes)
Voraussetzungen / Besonderes- We assume a good working knowledge about multiple linear regression ("the general linear model') and an intermediate (not beginner's) knowledge about model based statistics (estimation, confidence intervals,..).

Typically this means at least two classes of (math based) statistics, say
1. Intro to probability and statistics
2. (Applied) regression including Matrix-Vector notation Y = X b + E

- Basic (1 semester) "Matrix calculus" / linear algebra is also assumed.

- If familiarity with [R](Link) is not given, it should be acquired during the course (by the student on own initiative).
447-6236-00LStatistics for Survival Data Belegung eingeschränkt - Details anzeigen W2 KP1V + 1UA. Hauser
KurzbeschreibungThe primary purpose of a survival analysis is to model and analyze time-to-event data; that is, data that have as a principal endpoint the length of time for an event to occur. This block course introduces the field of survival analysis without getting too embroiled in the theoretical technicalities.
LernzielPresented here are some frequently used parametric models and methods, including accelerated failure time models; and the newer nonparametric procedures which include the Kaplan-Meier estimate of survival and the Cox proportional hazards regression model. The statistical tools treated are applicable to data from medical clinical trials, public health, epidemiology, engineering, economics, psychology, and demography as well.
InhaltThe primary purpose of a survival analysis is to model and analyze time-to-event data; that is, data that have as a principal endpoint the length of time for an event to occur. Such events are generally referred to as "failures." Some examples are time until an electrical component fails, time to first recurrence of a tumor (i.e., length of remission) after initial treatment, time to death, time to the learning of a skill, and promotion times for employees.

In these examples we can see that it is possible that a "failure" time will not be observed either by deliberate design or due to random censoring. This occurs, for example, if a patient is still alive at the end of a clinical trial period or has moved away. The necessity of obtaining methods of analysis that accommodate censoring is the primary reason for developing specialized models and procedures for failure time data. Survival analysis is the modern name given to the collection of statistical procedures which accommodate time-to-event censored data. Prior to these new procedures, incomplete data were treated as missing data and omitted from the analysis. This resulted in the loss of the partial information obtained and in introducing serious systematic error (bias) in estimated quantities. This, of course, lowers the efficacy of the study. The procedures discussed here avoid bias and are more powerful as they utilize the partial information available on a subject or item.

This block course introduces the field of survival analysis without getting too embroiled in the theoretical technicalities. Models for failure times describe either the survivor function or hazard rate and their dependence on explanatory variables. Presented here are some frequently used parametric models and methods, including accelerated failure time models; and the newer nonparametric procedures which include the Kaplan-Meier estimate of survival and the Cox proportional hazards regression model. The statistical tools treated are applicable to data from medical clinical trials, public health, epidemiology, engineering, economics, psychology, and demography as well.
401-8628-00LSurvival Analysis (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: STA425

Beachten Sie die Einschreibungstermine an der UZH: Link
W3 KP1.5GUni-Dozierende
KurzbeschreibungThe analysis of survival times, or in more general terms, the analysis
of time to event variables is concerned with models for censored
observations. Because we cannot always wait until the event of interest
actually happens, the methods discussed here are required for an
appropriate handling of incomplete observations where we only know that
the event of interest did not happen within ...
Lernziel
InhaltDuring the course, we will study the most important methods and models
for censored data, including
- general concepts of censoring,
- simple summary statistics,
- estimation of survival curves,
- frequentist inference for two and more groups, and
- regression models for censored observations
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