STK9170 – Bootstrapping and resampling
Course description
Schedule, syllabus and examination date
Course content
A somewhat ambitious statistical aim is to replace formulae with computer power. If one needs to estimate the standard deviation for the average statistic Xn, then one may of course use the explicit formula s/sqrt(n). An alternative is to simulate 1000 pseudo-realisations of Xn from pseudo-datasets with properties resembling the original dataset, e.g. via resampling, and then compute the empirical standard deviation for these1000 pseudo-averages. This turns out to be a fruitful idea with far-reaching consequences. Methods similar to the one sketched above are called bootstrapping, and generally involve simulations of pseudo-datasets from an estimated model. The power of the methods is that they may be applied with more or less the same ease in rather more complicated models (than the simple nonparametric one above), with arbitrary statistics (not only the Xn above), and with general and potentially complicated measures of spread (instead of merely sd{Xn}). They may in particular be used in situations where explicit formulae cannot be derived. The course focuses on the general theory for bootstrapping and jackknifing, for estimation and for construction of simulation based confidence intervals. Students need to apply both halves of their brains, as the theory is being used also with practical exercises and computers. The course aims at being practically useful.
Learning outcome
The student learns to solve classes of inference problems via boostrapping or related resampling methods. The tools include stochastic simulation, suitably coupled with insights in statistical modelling.
Admission
PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.
If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.
PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.
Prerequisites
Recommended previous knowledge
STK1100 – Probability and Statistical Modelling, STK1100 – Probability and Statistical Modelling, STK2120 – Statistical Methods and Data Analysis 2 (discontinued)
Overlapping courses
10 credits overlap with STK4170 – Bootstrapping and resampling (discontinued)
For information about the potential partial overlap with other courses, contact the Department.
Teaching
3 hours of lectures/exercises per week throughout the semester.
Examination
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.
In addition, each phd student is expected to give a one hour oral presentation on a topic of relevance (chosen in cooperation with the lecturer). The presentation has to be approved by the lecturer for the student to be admitted to the final exam.
Examination support material
No examination support material is allowed.
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.
Grading scale
Grades are awarded on a pass/fail scale. 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.
Evaluation
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.