Suchergebnis: Katalogdaten im Frühjahrssemester 2020

DAS in Angewandter Statistik Information
Wahlfächer
NummerTitelTypECTSUmfangDozierende
447-0102-02LAngewandte Multivariate Statistik II Belegung eingeschränkt - Details anzeigen
Nur für DAS und CAS in Angewandter Statistik.
W3 KP1V + 1UB. Sick
KurzbeschreibungSpezialisierte Methoden der multivariaten Statistik: Klassifikation, Baummodelle, Support Vector Machines, Neural Networks.
LernzielDie multivariate Statistik behandelt die gemeinsame Verteilung von mehreren Zufallsvariablen. Der Kurs führt spezialisierte Konzepte ein.
447-6624-02LApplied Time Series II Belegung eingeschränkt - Details anzeigen
Nur für DAS und CAS in Angewandter Statistik.
W4 KP1V + 1UM. Dettling
KurzbeschreibungMore advanced topics in time series analysis like time series regression, state space models and spectral analysis.
LernzielGetting to know advanced methods and software that are necessary such that the student can independently run an applied time series analysis.
SkriptA script will be available.
447-6222-01LRobust Regression Belegung eingeschränkt - Details anzeigen
Nur für DAS und CAS in Angewandter Statistik.
W1 KPA. F. Ruckstuhl
KurzbeschreibungThe basic ideas of robust fitting techniques are explained theoretically and practically using regression models and explorative multivariate analysis.
LernzielParticipants 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.
InhaltInfluence 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.
LiteraturLecture notes are available.
447-6222-02LNonlinear Regression Belegung eingeschränkt - Details anzeigen
Nur für DAS und CAS in Angewandter Statistik.
W1 KPA. F. Ruckstuhl
KurzbeschreibungFitting nonlinear regression functions and determining reliable confidence intervals.
LernzielParticipants 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.
InhaltNonlinear regression models, estimation methods, approximate tests and confidence intervals, estimation methods, profile t plot, profile traces, parameter transformations, prediction and calibration.
SkriptLecture notes are available.
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.
  •  Seite  1  von  1