7.5 credits cr.
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This course gives you an in-depth understanding of basic statistical principles, such as the principles of sufficiency, ancilliarity, invariance, and conditionality. Bayesian, likelihood-basedand Neyman-Pearson inference are applied and exemplified through point-estimation, interval estimation, and hypothesis testing.
You will learn about important theorems in inference theory and convergence-properties of estimators. You will also learn how to derive important point estimators, interval estimators, and test statistics in some selected applications.
The course provides a solid base for research studies in statistics.
The course is given at day time, full time.
The course forms a part of the Master's Program in Statistics, but it can also be studied as a freestanding course.
The teaching forms consist of lectures and exercises.
More information for registered students will be found in Athena.
Examination will be in the form of written and oral examination.
ScheduleThe schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
Course literatureNote that the course literature can be changed up to two months before the start of the course.