STK4290 - Selected Themes in Advanced Statistics
The precise contents of this course may vary from occasion to occasion, but will consist of selected themes of contemporary research interest in statistics methodology, depending on both demands from students and the availability of appropriate course leaders. Examples include parametric lifetime modelling, experimental design, extreme value statistics, advanced stochastic simulation, graphical modelling. The course will be of interest to students who want to develop their basic knowledge of statistics methodology. See the specific semester page for a more detailed description of the course.
Spring 2014: Parametric Lifetime Modeling
The course gives an introduction to modeling and statistical analysis of lifetimes, with emphasis on parametric models and estimation techniques, and applications in technical reliability and medicine. Topics in lifetime modeling include: the survival function; the hazard function; the mean residual life function, as well as different types of censoring of lifetimes. The most common parametric lifetime models are considered, in particular the accelerated failure time models and the proportional hazards models for survival regression. Estimation is mainly by maximum likelihood, but some commonly used non-parametric methods will be briefly considered for comparison. Special topics that will be covered are competing risks and recurrent event models.
Students will become familiar with the themes in question and develop knowledge of statistical methods, and will also learn how the methodology becomes relevant in certain application areas.
Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.
If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.
Recommended previous knowledge
STK1100 - Probability and statistical modelling, STK1110 - Statistical methods and data analysis 1 and one of the following courses: STK2100 - Machine learning and statistical methods for prediction and classification, STK2120 - Statistical Methods and Data Analysis 2 (discontinued) or STK3100 - Introduction to generalized linear models
10 credits overlap with STK9290 - Selected Themes in Advanced Statistics
For information about the potential partial overlap with other courses, contact the Department.
3 hours of lectures/exercises on average per week. The course may however not be teached weekly.
Depending on the number of students, the exam will be in one of the following four forms:
1. Only written exam
2. Only oral exam
3. A project paper followed by a written exam.
4. A project paper followed by an oral exam/hearing.
For the latter two the project paper and the exam counts equally and the final grade is based on a general impression after the final exam. (The two parts of the exam will not be individually graded.)
What form the exam will take will be announced by the teaching staff within October 15th for the autumn semester and March 15th for the spring semester.
Examination support material
Permitted aids at the exam if written: Approved calculator.
Oral exam: No aids permitted.
Language of examination
Subjects taught in English will only offer the exam paper in English.
You may write your examination paper in Norwegian, Swedish, Danish or English.
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Explanations and appeals
Resit an examination
This course offers both postponed and resit of examination. Read more:
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.