Search result: Catalogue data in Autumn Semester 2019

Number	Title	Type	ECTS	Hours	Lecturers
CAS in Applied Statistics
Compulsory Courses
447-0649-01L	Applied Statistical Regression I Only for DAS and CAS in Applied Statistics.	O	4 credits	1V + 1U	M. Tanadini
Abstract	Simple and multiple regression models, with emphasis on practical aspects and interpretation of results, analysis of residuals and model selection.
Learning objective	Understanding the multiple linear regression model and its importance for modelling and prediction. Practice of regression analyses using the statistical software R and correct interpretation of results. Model critique by analysis of residuals. Strategies for model selection.
447-0625-01L	Applied Analysis of Variance and Experimental Design I Only for DAS and CAS in Applied Statistics.	O	3 credits	1V + 1U	L. Meier
Abstract	Principles of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs.
Learning objective	Participants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
Literature	G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.

Further Courses
Number	Title	Type	ECTS	Hours	Lecturers
447-0649-02L	Applied Statistical Regression II Only for DAS and CAS in Applied Statistics.	Z	2 credits	1V + 1U	J. Ernest
Abstract	Generalized linear models (GLMs) and basic ideas of robust regression.
Learning objective	Understanding the concept and flexibility of generalized linear models and correct interpretation of the corresponding model outputs.
447-0625-02L	Applied Analysis of Variance and Experimental Design II Only for DAS and CAS in Applied Statistics.	Z	3 credits	1V + 1U	L. Meier
Abstract	Random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
Learning objective	Participants will be able to plan and analyze sophisticated experiments in the fields of natural sciences. They will gain practical experience by using the software R.
Literature	G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
447-6221-00L	Nonparametric Regression Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.	W	1 credit	1G
Abstract	This course focusses on nonparametric estimation of probability densities and regression functions. These recent methods allow modelling without restrictive assumptions such as 'linear function'. These smoothing methods require a weight function and a smoothing parameter. Focus is on one dimension, higher dimensions and samples of curves are treated briefly. Exercises at the computer.
Learning objective	Knowledge on estimation of probability densities and regression functions via various statistical methods. Understanding of the choice of weight function and of the smoothing parameter, also done automatically. Practical application on data sets at the computer.
447-6257-00L	Repeated Measures Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.	W	1 credit	1G
Abstract	Generation and structure of repeated measures. Planning and realization of corresponding studies. Within- and between-subjects factors. Common covariance structures. Statistical analyses: graphical methods, summary statistics approach, univariate and multivariate ANOVA, linear mixed effects models.
Learning objective	Participants will gain the ability of recognizing repeated measures and to analyze them adequately. They will know how to deal with pseudoreplicates.
Lecture notes	Es wird ein Skript abgegeben.
447-6289-00L	Sampling Surveys Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.	W	2 credits	1G
Abstract	The elements of a sample survey are explained. The most important classical sample designs (simple random sampling and stratified random sampling) with their estimation procedures and the use of auxiliary information including the Horvitz-Thompson estimator are introduced. Data preparation, non-response and its treatment, variance estimation and analysis of survey data is discussed.
Learning objective	Knowledge of the Elements and the process of a sample survey. Understanding of the paradigm of random samples. Knowledge of simple random samplinig and stratified random sampling and capability to apply the corresponding methods. Knowledge of further methods of sampling and estimation as well as data preparation and analysis.
447-6201-00L	Nonparametric and Resampling Methods Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.	Z	2 credits	2G	L. Meier, D. Kuonen
Abstract	Nonparametric tests, randomization tests, jackknife and bootstrap, as well as asymptotic properties of estimators.
Learning objective	For classical parametric models there exist optimal statistical estimators and test statistics whose distributions can often be determined exactly. The methods covered in this course allow for finding statistical procedures for more general models and to derive exact or approximate distributions of complicated estimators and test statistics.
Content	Nonparametric tests, randomization tests, jackknife and bootstrap, as well as asymptotic properties of estimators.
Prerequisites / Notice	This course is part of the programme for the certificate and diploma in Advanced Studies in Applied Statistics. It is given every second year in the winter semester break.
447-6233-00L	Spatial Statistics Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.	W	1 credit	1G
Abstract	In many research fields, spatially referenced data are collected. When analysing such data the focus is either on exploring their structure (dependence on explanatory variables, autocorrelation) and/or on spatial prediction. The course provides an introduction to geostatistical methods that are useful for such purposes.
Learning objective	The course will provide an overview of the basic concepts and stochastic models that are commonly used to model spatial data. In addition, the participants will learn a number of geostatistical techniques and acquire some familiarity with software that is useful for analysing spatial data.
Content	After an introductory discussion of the types of problems and the kind of data that arise in environmental research, an introduction into linear geostatistics (models: stationary and intrinsic random processes, modelling large-scale spatial patterns by regression, modelling autocorrelation by variogram; kriging: mean-square prediction of spatial data) will be taught. The lectures will be complemented by data analyses that the participants have to do themselves.
Lecture notes	Slides, descriptions of the problems for the data analyses and worked-out solutions to them will be provided.
Literature	P.J. Diggle & P.J. Ribeiro Jr. 2007. Model-based Geostatistics. Springer
447-6245-00L	Data Mining Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.	W	1 credit	1G
Abstract	Block course only on prediction problems, aka "supervised learning". Part 1, Classification: logistic regression, linear/quadratic discriminant analysis, Bayes classifier; additive and tree models; further flexible ("nonparametric") methods. Part 2, Flexible Prediction: additive models, MARS, Y-Transformation models (ACE,AVAS); Projection Pursuit Regression (PPR), neural nets.
Learning objective
Content	"Data Mining" is a large field from which in this block course, we only treat so called prediction problems, aka "supervised learning". Part 1, Classification, recalls logistic regression and linear / quadratic discriminant analysis (LDA/QDA) and extends these (in the framework of 'Bayes classifier") to (generalized) additive (GAM) and tree models (CART), and further mentions other flexible ("nonparametric") methods. Part 2, Flexible Prediction (of continuous or "class" response/target) contains additive models, MARS, Y-Transformation models (ACE, AVAS); Projection Pursuit Regression (PPR), neural nets.
Lecture notes	The block course is based on (German language) lecture notes.
Prerequisites / Notice	The exercises are done exlusively with the (free, open source) software "R" (http://www.r-project.org). A final exam will also happen at the computers, using R (and your brains!).
447-6273-00L	Bayes Methods Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.	W	2 credits	2G
Abstract	conditional probability; bayes inference (conjugate distributions, HPD-areas; linear and empirical bayes); determination of the a-posteriori distribution through simulation (MCMC with R2Winbugs); introduction to multilevel/hierarchical models.
Learning objective
Content	Bayes statistics is attractive, because it allows to make decisions under uncertainty where a classical frequentist statistical approach fails. The course provides an introduction into bayesian methods. It is moderately mathematically technical, but demands a flexibility of mind, which should not underestimated.
Literature	Gelman A., Carlin J.B., Stern H.S. and D.B. Rubin, Bayesian Data Analysis, Chapman and Hall, 2nd Edition, 2004. Kruschke, J.K., Doing Bayesian Data Analysis, Elsevier2011.
Prerequisites / Notice	Prerequisite:Basic knowledge of statistics; Knowledge of R.
447-6191-00L	Statistical Analysis of Financial Data Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.	W	2 credits	1G
Abstract	Distributions for financial data. Volatility models: ARCH- and GARCH models. Value at risk and expected shortfall. Portfolio theory: minimum-variance portfolio, efficient frontier, Sharpe’s ratio. Factor models: capital asset pricing model, macroeconomic factor models, fundamental factor model. Copulas: Basic theory, Gaussian and t-copulas, archimedean copulas, calibration of copulas.
Learning objective	Getting to know the typical properties of financial data and appropriate statistical models, incl. the corresponding functions in R.

Page 1 of 1