Study information

Statistical Modelling in Space and Time - 2020 entry

MODULE TITLEStatistical Modelling in Space and Time CREDIT VALUE15
MODULE CODEMTHM033 MODULE CONVENERProf Peter Challenor (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 0
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content

Previous modules in statistics have treated data as independent and identically distributed, but real world data is not like that. In particular data collected in space and time can be highly correlated. In this module you will look at methods of modelling such dependent data. Furthermore, you will examine how to model data as a field in n-dimensions, and the particular problems associated with time series.

AIMS - intentions of the module

In many applications of statistics data are referenced by space and time. Points that are close together are correlated so we cannot use methods that assume they are independent. In this module you will learn methods for modelling correlated data in one, two and higher dimensions as well as modelling time series. Although we will explain the theory in detail, we will concentrate on the real world, including examples from computer modelling, the environment and health.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
On successful completion of this module, you should be able to:
 
Module Specific Skills and Knowledge:
1 Model correlated data structures in continuous space and time co-ordinates;
2 Explain what a Gaussian process is and how it can be used to model spatially correlated data in 1, 2 or many dimensions;
3 Describe the difference between space and time in modelling and create models using both ARIMA and state space modelling of time series; 4 Demonstrate an understanding of spatio-temporal modelling;
5 Use appropriate software and a suitable computer language for modelling correlated data in space, time and both together;
 
Discipline Specific Skills and Knowledge:
6 Apply the theory of statistical modeling of spatially and temporally correlated data and analyse the resulting models;
 
Personal and Key Transferable / Employment Skills and Knowledge:
7 Utilise advanced data analysis skills and be able to communicate associated reasoning and interpretations effectively in writing; 8 Use relevant computer software competently;
9 Utilise learning resources appropriately.
 
SYLLABUS PLAN - summary of the structure and academic content of the module

- Dependent data; distance and correlation, stationarity, the Gaussian process; covariance functions; nuggets, sampling from Gaussian processes;

- Types of covariance function, Bochner’s theorem; separability; fitting Gaussian processes; examples;

- Kriging; variograms and covariance functions; time and space; ARIMA models; state space models; dynamic linear models;

- Spatio-temporal models, hierarchical modelling.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33 Guided Independent Study 115 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 22 Lectures
Scheduled Learning and Teaching Activities 11 Tutorials
Guided Independent Learning 115 Coursework, background reading, preparation for contact time, preparation for assessments

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework – computer modelling exercises and theoretical problems, 1-3 10 hours per set 1-3, 5-9 Written and oral

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 20 Written Exams 80 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written Exam (Closed Book) 80 2 hours 1-7, 9 Written/verbal on request
Coursework – practical modelling exercises and theoretical problems 20 10 hours 1-10 Written and oral

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
As Above     August Ref/Def Period

 

RE-ASSESSMENT NOTES

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.

If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 50% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

ELE: http://vle.exeter.ac.uk

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Cressie, N. Statistics for Spatial Data Wiley 1991 000-0-471-84336-9
Set Shumway, R H, Stoffer, D S Time series analysis and its applications Springer 2015 978-1-4419-7865-3
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10th July 2018 LAST REVISION DATE Tuesday 8th September 2020
KEY WORDS SEARCH Statistics; Modelling; Gaussian Process; ARIMA

Please note that all modules are subject to change, please get in touch if you have any questions about this module.