MATH4341: Spatio-Temporal Statistics

• Advanced Statistical Modelling (MATH3411) OR Bayesian Computation and Modelling (MATH3421)

• None

• None

• To introduce the theory, computation and practice of the statistical analysis of problems involving aspects of space and time.

• Spatial statistics: introduction to regionalized statistical concepts (variable, stationarity, random functions, variograms); geostatistics, (co-)kriging; spatial models on lattices; aerial data analysis; point pattern data analysis; and computations with INLA.
• Temporal statistics and time series: components and properties of time series; local and moving-average methods; ARMA models; spectral anlaysis; forecasting and inference; state-space models.

Subject-specific Knowledge:

• Be able to identify, explain, and theoritise spatial and temporal dependencies in statistical problems.
• Be able to explain, apply, and generalize appropriate statistical methodology to address spatio-temporal problems.
• Be able to design, explain, interpret, and extend statistical models appropriate for spatial and/or temporal data sets, as well as make inferences and draw conclusions from the analysis of such models.
• Have a coherent understanding of the theory, computation and application of the mathematics underlying the introduced statistical models and methods.
• Be able to use appropriate software to facilitate spatio-temporal statistical analysis.

Subject-specific Skills:

• Students will have specialised statistical skills in the following areas which can be used with minimal guidance: modelling, use of software, analysis of datasets on different domains (space/time).

Key Skills:

• Students will have advanced skills in the following areas: statistical modelling, problem formulation and solution, critical and analytical thinking.

• 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.
• Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for the summative assessment.
• The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.

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