Search result: Catalogue data in Spring Semester 2020

DAS in Applied Statistics Information
Compulsory Courses and Supplements
NumberTitleTypeECTSHoursLecturers
447-0000-00LIntroduction to Probability and Statistics Restricted registration - show details
Does not take place this semester.
Only for DAS and CAS in Applied Statistics.
O4 credits2V + 2U
AbstractIntroduction to simple methods and fundamental concepts of probability and statistics. The concepts are introduced using example data.
ObjectiveGrundverständnis für die Gesetze des Zufalls und des Denkens in Wahrscheinlichkeiten. Kenntnis von Methoden zur Darstellung von Daten und zu ihrer quantitativen Interpretation unter Berücksichtigung der statistischen Unsicherheit.
ContentModelle und Statistik für Zähldaten:
Diskrete Wahrscheinlichkeitsmodelle, Binomialverteilung, Tests und Vertrauensintervalle für eine Wahrscheinlichkeit, Poisson-Verteilung und deren Statistik, weitere Verteilungen.
Modelle und Statistik für Messdaten:
Beschreibende Statistik, Zufallsvariablen mit Dichten, t-Test und Wilcoxon-Test und zugehörige Vertrauensintervalle.
447-0000-01LIntroduction to R Restricted registration - show details
Does not take place this semester.
Only for DAS and CAS in Applied Statistics.
Z0 credits1V + 2U
AbstractIntroduction to R: data import, basic data manipulation, and data visualisation.
ObjectiveThe students will be able to use R for simple data analysis.
447-0102-01LApplied Multivariate Statistics I Restricted registration - show details
Only for DAS and CAS in Applied Statistics.
O3 credits1V + 1UB. Sick
AbstractVisualization techniques, principal component analysis, MDS and t-SNE. Hierarchical clustering, k-means clustering.
ObjectiveMultivariate Statistics deals with joint distributions of several random variables. The course introduces the basic concepts and provides an overview of classical and modern methods of multivariate statistics and their applications.
447-6624-01LApplied Time Series I Restricted registration - show details
Only for DAS and CAS in Applied Statistics.
O2 credits1V + 1UM. Dettling
AbstractIntroduction to time series analysis: examples, goals and mathematical notation. Descriptive techniques, modelling and prediction.
ObjectiveGetting to know the mathematical properties of time series, as well as the requirements, descriptive techniques, models and software that are necessary such that the student can independently run an applied time series analysis.
ContentThe course starts with an introduction to time series analysis that comprises of examples and goals. We continue with notation and descriptive analysis of time series. A major part of the course will be dedicated to modeling and forecasting of time series using the flexible class of ARMA models.
Lecture notesA script will be available.
447-0990-00LWorkshop Restricted registration - show details
Only for DAS in Applied Statistics.
O1 credit1SL. Meier
AbstractIn the workshop each participant gives a short talk about a recent statistical problem encountered in their daily work.
ObjectivePresentation of a statistical problem, getting to know different applications of statistical methodology.
Electives
NumberTitleTypeECTSHoursLecturers
447-0102-02LApplied Multivariate Statistics II Restricted registration - show details
Only for DAS and CAS in Applied Statistics.
W3 credits1V + 1UB. Sick
AbstractSpecialized methods of multivariate statistics: Classification, tree-based models, support vector machines, neural networks.
ObjectiveMultivariate Statistics deals with joint distributions of several random variables. The course introduces more advanced concepts.
447-6624-02LApplied Time Series II Restricted registration - show details
Only for DAS and CAS in Applied Statistics.
W4 credits1V + 1UM. Dettling
AbstractMore advanced topics in time series analysis like time series regression, state space models and spectral analysis.
ObjectiveGetting to know advanced methods and software that are necessary such that the student can independently run an applied time series analysis.
Lecture notesA script will be available.
447-6222-01LRobust Regression Restricted registration - show details
Only for DAS and CAS in Applied Statistics.
W1 creditA. F. Ruckstuhl
AbstractThe basic ideas of robust fitting techniques are explained theoretically and practically using regression models and explorative multivariate analysis.
ObjectiveParticipants are familiar with common robust fitting methods for linear regression models as well as for exploratory multivariate analysis and are able to assess their suitability for the data at hand.
ContentInfluence function, breakdown point, regression M-estimation, regression MM-estimation, robust inference, covariance estimation with high breakdown point, application in principal component analysis and linear discriminant analysis.
LiteratureLecture notes are available.
447-6222-02LNonlinear Regression Restricted registration - show details
Only for DAS and CAS in Applied Statistics.
W1 creditA. F. Ruckstuhl
AbstractFitting nonlinear regression functions and determining reliable confidence intervals.
ObjectiveParticipants know the challenges that arise in fitting nonlinear regression functions. In addition, they are aware of the difference between classical and profile based methods to determine confidence intervals.
ContentNonlinear regression models, estimation methods, approximate tests and confidence intervals, estimation methods, profile t plot, profile traces, parameter transformations, prediction and calibration.
Lecture notesLecture notes are available.
447-6236-00LStatistics for Survival Data Restricted registration - show details W2 credits1V + 1UA. Hauser
AbstractThe 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.
ObjectivePresented 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.
ContentThe 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.
Diploma Thesis
NumberTitleTypeECTSHoursLecturers
447-1990-00LDiploma Thesis Restricted registration - show details
Only for DAS in Applied Statistics.
O2 credits4DSupervisors
AbstractThe diploma thesis typically consists of a data analysis of data from a participant's own field of work. The thesis requires a time expenditure of about one or two weeks. Thesis work should prove the participants' capability to apply useful and modern statistical methods to address appropriate questions properly and effectively.
ObjectiveThesis work should prove the participants' capability to apply useful and modern statistical methods to address appropriate questions properly and effectively.
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