This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2024-2025 (archived)
Module MATH4391: Nonparametric Statistics IV
Department: Mathematical Sciences
MATH4391: Nonparametric Statistics IV
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 4 | 20 | Available in 2024/2025 | None. | Durham |
Pre-requisites
- Statistical Inference (MATH2711)
Corequisites
- None
Excluded Combinations
- None
Aims
- To provide an overview on advanced nonparametric and distribution-free statistical methods and explain their principles through selected inferential problems both in the frequentist and the Bayesian framework.
Content
- Nonparametric density estimation.
- Nonparametric regression and smoothing techniques.
- Additive and semi-parametric models.
- Resampling methods.
- Distribution-free methods.
- Bayesian nonparametrics.
Learning Outcomes
Subject-specific Knowledge
- Identify and explain applications where nonparametric statistical approaches are appropriate.
- Explain and compare fundamental principles and basic properties of nonparametric techniques.
- Select, justify, generalise, and apply appropriate non-parametric tools for modelling and analysis of real applications and datasets.
- Implement non-parametric tools in computer programming language to generate output.
Subject-specific Skills
- Students will develop advanced statistical skills and relevant mathematical skills in modelling and computation.
Key Skills
- Students will develop advanced skills in problem solving, critical and analytical thinking, and communicating scientific results by written reports.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
- Computer practicals consolidate the studied material, explore theoretical ideas in practice, enhance practical understanding, and develop practical data analysis skills.
- Problem classes show how to solve example problems in an ideal way, revealing also the thought processes behind such solutions.
- Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
- Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for the summative assessment.
- The assignments assess the ability to implement the concepts introduced in the module using statistical software, to apply them in the analysis of a realistic problem, and to report scientific outputs in a clear and structured way.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 42 | Two per week in weeks 1-10, 11-20, 21 | 1 hour | 42 | |
| Computer Practicals | 6 | Weeks 3, 5, 7, 13, 15, 17 | 1 hour | 6 | Yes |
| Problem Class | 2 | Week 9, 19 | 1 hour | 2 | |
| Preparation and reading | 150 | ||||
| Total | 200 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Coursework | 100% | Assignment 1 | 50% | ||
| Assignment 2 | 50% |
Formative Assessment
Regular assignments to be formatively assessed and returned with feedback. Other problems are set for self-study and complete solutions are made available to students.
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.