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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2015/2016
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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Nonparametric Regression (MATH10052)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryCourse 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.
Course description Not entered
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: ( Several Variable Calculus and Differential Equations (MATH08063) AND Fundamentals of Pure Mathematics (MATH08064)) AND ( Probability (MATH08066) OR Probability with Applications (MATH08067)) AND ( Statistics (Year 2) (MATH08051) OR Statistics (Yr 3) (MATH09022)) AND Honours Complex Variables (MATH10067)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Seminar/Tutorial Hours 5, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 69 )
Assessment (Further Info) Written Exam 95 %, Coursework 5 %, Practical Exam 0 %
Additional Information (Assessment) Coursework 5%, Examination 95%
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Nonparametric Regression2:00
Learning Outcomes
1. Knowledge of methods for nonparametric regression and ability to apply them.
2. Familiarity with the Bayesian approach in wavelet nonparametric regression.
3. Ability to use R to fit a nonparametric regression model.
Reading List
None
Additional Information
Course URL http://student.maths.ed.ac.uk
Graduate Attributes and Skills Not entered
KeywordsNPR
Contacts
Course organiserDr Natalia Bochkina
Tel: 0131 650 8597
Email: 		Course secretaryMrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email:
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