Search result: Catalogue data in Spring Semester 2020

Number	Title	Type	ECTS	Hours	Lecturers
CAS in Applied Statistics
Compulsory Courses
447-0000-00L	Introduction to Probability and Statistics Does not take place this semester. Only for DAS and CAS in Applied Statistics.	O	4 credits	2V + 2U
Abstract	Introduction to simple methods and fundamental concepts of probability and statistics. The concepts are introduced using example data.
Objective	Grundverständnis 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.
Content	Modelle 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.
447-0102-01L	Applied Multivariate Statistics I Only for DAS and CAS in Applied Statistics.	O	3 credits	1V + 1U	B. Sick
Abstract	Visualization techniques, principal component analysis, MDS and t-SNE. Hierarchical clustering, k-means clustering.
Objective	Multivariate 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.
447-6624-01L	Applied Time Series I Only for DAS and CAS in Applied Statistics.	O	2 credits	1V + 1U	M. Dettling
Abstract	Introduction to time series analysis: examples, goals and mathematical notation. Descriptive techniques, modelling and prediction.
Objective	Getting 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.
Content	The 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 notes	A script will be available.
446-0990-00L	Interview Only for CAS in Applied Statistics.	O	0 credits		Supervisors
Abstract	In the interview participants present and discuss the application of statistical methodology in an area of application determined by the participants.
Objective	Presentation and discussion of statistical questions of the own area of expertise.

Further Courses
Number	Title	Type	ECTS	Hours	Lecturers
447-0000-01L	Introduction to R Does not take place this semester. Only for DAS and CAS in Applied Statistics.	Z	0 credits	1V + 2U
Abstract	Introduction to R: data import, basic data manipulation, and data visualisation.
Objective	The students will be able to use R for simple data analysis.
447-0102-02L	Applied Multivariate Statistics II Only for DAS and CAS in Applied Statistics.	W	3 credits	1V + 1U	B. Sick
Abstract	Specialized methods of multivariate statistics: Classification, tree-based models, support vector machines, neural networks.
Objective	Multivariate Statistics deals with joint distributions of several random variables. The course introduces more advanced concepts.
447-6624-02L	Applied Time Series II Only for DAS and CAS in Applied Statistics.	W	4 credits	1V + 1U	M. Dettling
Abstract	More advanced topics in time series analysis like time series regression, state space models and spectral analysis.
Objective	Getting to know advanced methods and software that are necessary such that the student can independently run an applied time series analysis.
Lecture notes	A script will be available.
447-6222-01L	Robust Regression Only for DAS and CAS in Applied Statistics.	W	1 credit		A. F. Ruckstuhl
Abstract	The basic ideas of robust fitting techniques are explained theoretically and practically using regression models and explorative multivariate analysis.
Objective	Participants 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.
Content	Influence 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.
Literature	Lecture notes are available.
447-6222-02L	Nonlinear Regression Only for DAS and CAS in Applied Statistics.	W	1 credit		A. F. Ruckstuhl
Abstract	Fitting nonlinear regression functions and determining reliable confidence intervals.
Objective	Participants 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.
Content	Nonlinear regression models, estimation methods, approximate tests and confidence intervals, estimation methods, profile t plot, profile traces, parameter transformations, prediction and calibration.
Lecture notes	Lecture notes are available.
447-6236-00L	Statistics for Survival Data	W	2 credits	1V + 1U	A. Hauser
Abstract	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.
Objective	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.
Content	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.

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