Suchergebnis: Katalogdaten im Frühjahrssemester 2020
Statistik Master 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 | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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401-8618-00L | Statistical 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 | W | 5 KP | 3G | Uni-Dozierende | |
Kurzbeschreibung | Analysis 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 | ||||||
401-4626-00L | Advanced Statistical Modelling: Mixed Models | W | 4 KP | 2V | M. Mächler | |
Kurzbeschreibung | Mixed 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. | |||||
Inhalt | The 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. | |||||
Skript | We 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). | |||||
447-6236-00L | Statistics for Survival Data | W | 2 KP | 1V + 1U | A. Hauser | |
Kurzbeschreibung | The 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. | |||||
Lernziel | 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. | |||||
Inhalt | The 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. | |||||
401-8628-00L | Survival Analysis (University of Zurich) Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: STA425 Beachten Sie die Einschreibungstermine an der UZH: Link | W | 3 KP | 1.5G | Uni-Dozierende | |
Kurzbeschreibung | The 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 | ||||||
Inhalt | During 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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