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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2014/2015
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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
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) Credits10
Home subject areaMathematics Other subject areaSpecialist Mathematics & Statistics (Honours)
Course website http://student.maths.ed.ac.uk Taught in Gaelic?No
Course descriptionCourse 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Foundations of Calculus (MATH08005) AND Several Variable Calculus (MATH08006) AND Linear Algebra (MATH08007) AND Methods of Applied Mathematics (MATH08035) AND ( Probability (Year 2) (MATH08008) OR Probability (Year 3) (MATH09004)) AND ( Statistics (Year 2) (MATH08051) OR Statistics (Year 3) (MATH09021))
Co-requisites
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2014/15 Semester 2, Available to all students (SV1) Learn enabled:  Yes Quota:  None
Web Timetable Web Timetable
Course Start Date 12/01/2015
Breakdown of 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 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 95 %, Coursework 5 %, Practical Exam 0 %
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Nonparametric Regression2:00
Summary of Intended 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.
Assessment Information
See 'Breakdown of Assessment Methods' and 'Additional Notes', above.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus Not entered
Transferable skills Not entered
Reading list Not entered
Study Abroad Not entered
Study Pattern 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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