course-details-portlet

MA8701 - General Statistical Methods

About

Lessons are not given in the academic year 2015/2016

Course content

The course is usually given every second year, and only if a sufficient number of students register. It is given next time Spring 2017. If too few students register, the course is given as a guided self study.

The course provides a broad introduction to the basic principles and methods of statistical inference and prediction. Together with course MA8704 Probability theory and asymptotic techniques it provides a theoretical basis for PhD students in statistics.

The course includes: Introduction to supervised learning. Linear methods for regression and classification. Basic expansions and regularization. Kernel smoothing methods. Likelihood inference and asymptotic methods. Model inference, assessment and selection. Empirical Bayes methods.

Learning outcome

1. Knowledge.
The course provides a broad introduction to the basic principles and methods of statistical inference and prediction. Together with course MA8704 Probability theory and asymptotic techniques it provides a theoretical basis for PhD students in statistics. The course includes: Introduction to supervised learning. Linear methods for regression and classification. Basic expansions and regularization. Kernel smoothing methods. Likelihood inference and asymptotic methods. Model inference, assessment and selection. Empirical Bayes methods.

2. Skills
Students will learn about and be able to use basic techniques in modern statistics (statistical learning). Particular emphasis is placed on modern methods for analyzing large amounts of data (data mining), both using parametric and non-parametric methods; empirical Bayes methods; and model choice and derivation of asymptotic properties of the methods.

3. Competence
The students will be able to participate in scientific discussions and carry out research in statistics at high international level. They will be able to participate in applied projects involving statistical methods and to apply their knowledge to problems in theoretical statistics.

Learning methods and activities

Lectures, alternatively guided self-study.

Course materials

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics, 2009) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

More on the course
Facts

Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level

Coursework

No

Language of instruction: -

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Subject area(s)
  • Statistics
Contact information

Department with academic responsibility
Department of Mathematical Sciences

Phone:

Examination

  • * The location (room) for a written examination is published 3 days before examination date.
If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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