Undergraduate Course: Nonparametric Regression (MATH10052)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 10 (Year 4 Undergraduate)
||Availability||Available to all students
|Summary||Course for final year students in Honours programmes in Mathematics.
A regression function is an important tool for describing the relation between two or more random variables. In real life problems, this function is usually unknown but can be estimated from a sample of observations. Nonparametric methods are flexible techniques dedicated to treat general cases where the shape of the regression curve is unknown.
In this course we will introduce nonparametric regression methods such as kernel and spline smoothing, with emphasis on nonparametric wavelet regression. We will see how these methods can be applied in practice using R.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2015/16, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework 5%, Examination 95%
||Hours & Minutes
|Main Exam Diet S2 (April/May)||Nonparametric Regression||2:00|
On completion of this course, the student will be able to:
- Knowledge of methods for nonparametric regression and ability to apply them.
- Ability to study asymptotic properties of nonparametric estimators.
- Ability to use R to fit a nonparametric regression model.
|Course organiser||Dr Jonathan Gair
Tel: (0131 6)50 4897
|Course secretary||Mrs Alison Fairgrieve
Tel: (0131 6)50 5045
© Copyright 2015 The University of Edinburgh - 18 January 2016 4:24 am