Skip to main content
Module Search
A street in Durham, with old buildings and people walking down the street
 

MATH4391: Nonparametric Statistics IV

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box, and this may change from year to year, due to, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback.

Type Open
Level 4
Credits 20
Availability Available in 2025/2026
Module Cap None.
Location Durham
Department Mathematical Sciences

Prerequisites

  • Statistical Inference (MATH2711)

Corequisites

  • None

Excluded Combinations of Modules

  • 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.
  • Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
  • 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

ActivityNumberFrequencyDurationTotalMonitored
Lectures42Two per week in weeks 1-10, 11-20, 211 hour42 
Practicals8Weeks 3, 5, 7, 9, 13, 15, 17, 191 hour8Yes
Preparation and reading150 
Total200 

Summative Assessment

Component: CourseworkComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Assignment50
Assignment50

Formative Assessment

Problems are set for self-study and in practicals, and complete solutions are made available to students.

More information

If you have a question about Durham's modular degree programmes, please visit our FAQ webpages, Help page or our glossary of terms. If you have a question about modular programmes that is not covered by the FAQ, or a query about the on-line Undergraduate Module Handbook, please contact us.

Prospective Students: If you have a query about a specific module or degree programme, please Ask Us.

Current Students: Please contact your department.