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Statistical Inference

  • 7.5 credits

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.

  • Course structure

    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.

    Teaching format

    The teaching forms consist of lectures and exercises.

    Language: English.

    Course information

    More information for registered students will be found in Athena.

    Assessment

    Examination will be in the form of written and oral examination.

    Examiner

    Teachers autumn 2019

    Course coordinator

    Per Gösta Andersson

    You will find Per Gösta's reception hours in the link above. If you want to visit Per Göstaoutside of his reception hours, you are welcome to e-mail him for an appointment.

  • Contact

    If you have questions about the course, please contact the cours coordinator:

    Teachers autumn 2019

    Course coordinator

    Per Gösta Andersson

    You will find Per Gösta's reception hours in the link above. If you want to visit Per Göstaoutside of his reception hours, you are welcome to e-mail him for an appointment.

    If you have questions about studying at the Department of Statistics, please contact our study- and career counselor: Jenny Rosen.