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This occurrence is not offered in 2017
Survival and Longitudinal Data Analysis
Failure in mechanical systems, death or disease in biological organisms, occurrence of historical events – all these can be analysed using the branch of statistics known as survival analysis. The course will cover various censoring mechanisms, exploratory analysis and visualisation of event data, and various ways to model them, including Kaplan-Meier estimators, Cox regression, hierarchical models, and change-point analysis. R will be used for analysis of datasets from varying fields including epidemiology, engineering, biology and sociology. Students are encouraged, but not required, to know R beforehand.It is useful to know about regression (STAT202), generalised linear models (STAT319) and likelihood inference (STAT213) beforehand.
The course will enable studentsto summarize and visualize event datato apply Kaplan-Meier estimator in order to test for effect of different factors on the time-to-event outcometo apply Cox regression in order to model the time-to-event phenomenonto apply hierarchical models to longitudinal datato apply parametric survival models, e.g. accelerated failure time modelsto detect and estimate change-points in temporal processes
Subject to approval of the Head of School.
For further information see Mathematics and Statistics Head of Department
Mathematics and Statistics Honours Booklet General information for students LEARN
Domestic fee $932.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Mathematics and Statistics .