Course - General Statistical Methods - MA8701
MA8701 - General Statistical Methods
About
Examination arrangement
Examination arrangement: Written examination
Grade: Passed/Failed
Term:
Autumn
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Skriftlig | 100/100 | 4 timer |
Examination arrangement
Examination arrangement: Oral examination
Grade: Passed/Failed
Term:
Spring
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Muntlig | 100/100 |
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 2015. 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.
Recommended previous knowledge
TMA4267 Linear Statistical Methods, TMA4295 Statistical inference, TMA4300 Computer intensive statistical methods, or equivalent knowledge.
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.
Version: 1
Credits:
7.5 SP
Study level: Doctoral degree level
Term no.: 1
Teaching semester: SPRING 2015
Language of instruction: -
-
- Statistics
Department with academic responsibility
Department of Mathematical Sciences
Examination
Examination arrangement: Written examination
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn ORD Skriftlig 100/100
-
Room Building Number of candidates
Examination arrangement: Oral examination
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD Muntlig 100/100 2015-05-13
-
Room Building Number of candidates - Summer KONT Muntlig 100/100 2015-08-07
-
Room Building Number of candidates
- * 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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"