Undergraduate Course: Nonparametric Regression Models (MATH10104)

Course Outline
School	School of Mathematics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 10 (Year 4 Undergraduate)	Availability	Not available to visiting students
SCQF Credits	10	ECTS Credits	5
Summary	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 models and how these can be applied in practice using R.
Course description	Topics to be covered includes: - Splines; - General additive models; - Kernel estimation; - Wavelets; - Use of R for fitting non-parametric models.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	None

Course Delivery Information
Not being delivered

Learning Outcomes
On completion of this course, the student will be able to: Knowledge of a range of methods for nonparametric regression and be able to apply them The ability to study asymptotic properties of nonparametric estimators The ability to use R to fit nonparametric regression models