ADVANCED MATHEMATICAL STATISTICS - 2021/2
Module code: MATM055
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
MATM055 builds on the material on statistical inference, hypothesis testing and interval estimation encountered in modules at lower levels. Topics in point estimation encompass: comparison of estimators; the concept of sufficiency; identification of minimum variance unbiased estimators. Fundamental methods of estimation are covered. The construction of exact and approximate confidence intervals using pivots is developed. Aspects of hypothesis testing include the Neyman-Pearson lemma, uniformly most powerful tests and likelihood ratio tests.
WOLF Martin (Maths)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: G120
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 112
Lecture Hours: 33
Practical/Performance Hours: 5
Prerequisites / Co-requisites
Indicative content includes:
• Comparison of estimators and the Cramer-Rao lower bound.
• Sufficient statistics and complete sufficient statistics.
• Exponential families of distributions
Methods of Estimation:
• Method of moments
• Method of maximum likelihood estimators and their limiting distributions
• Method of least squares
• Confidence intervals
• Pivots and exact confidence intervals
• Asymptotic pivots and approximate confidence intervals
• Connection between confidence intervals and hypothesis tests
• Tests and confidence intervals for two sample problems
• Type I and type II errors
• Neyman-Pearson lemma
• Generalised likelihood ratio tests
• Wilks’ theorem
|Assessment type||Unit of assessment||Weighting|
|School-timetabled exam/test||Class test||20|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
• Analytical ability by solution of unseen problems in the test and exam.
• Subject knowledge through the recall of key definitions, theorems and their proofs.
Thus, the summative assessment for this module consists of:
• One two hour examination (students have the choice of three questions out of four to contribute to exam mark) at the end of the semester; weighted at 80% of the module mark.
• One class test; weighted at 20% of the module mark.
Formative assessment and feedback
Students receive written feedback via a number of marked unassessed coursework assignments over an 11 week period.
- Provide students with a detailed understanding of the principles of efficient estimation and hypothesis testing.
- Equip students with skills to be able to determine the quality of an estimator or test procedure.
|001||Demonstrate an advanced understanding of principles and theory of estimation and hypothesis testing.||CKT|
|002||Assess the properties of an estimator or test procedure using a number of criteria.||CKP|
|003||Construct estimators and test procedures based.||CKP|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to provide:
• A good grounding in statistical inference.
• Experience in problem solving for the cognitive skills.
The learning and teaching methods include:
• 3 x 1 hour lectures per week x 11 weeks.
• 5 x 1 hour practice classes, fortnightly.
• Several pieces of unassessed coursework to give students experience of using techniques introduced in the module and to receive formative feedback.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MATM055
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2021/2 academic year.