STU34502 – Applied Linear Statistical Methods II

This module will be offered again in the 2021/2022 Academic Year.

Module CodeSTU34502
Module Name Applied Linear Statistical Methods II
ECTS Weighting [1]5 ECTS
Semester taughtSemester 2
Module Coordinator/s Alessio Benavoli

Module Learning Outcomes

On successful completion of this module, students will be able to: 

  1. Demonstrate ways in which the multivariate linear regression model can be generalised to non-linear and non-Gaussian cases;
  1. Define the generalised linear model and implement an analysis with specific examples of this model;
  1. Motivate the use of deviance as a measure of model fit and its use in estimating prediction error;
  1. Define the Kalman Filter and derive the updating equations from Bayes Law and properties of the multivariate Gaussian distribution.

  2. Apply the Kalman Filter to object tracking and trading.

Module Content

The topics covered are: 

  • Recap of linear regression 
  • The exponential family of distributions 
  • The generalised linear model 
  • Specific examples: binomial, Poisson, logistic 
  • Deviance 
  • Applications and examples 
  • R programming

Teaching and learning Methods

Lectures 

Assessment Details

Assessment ComponentBrief Description Learning Outcomes Addressed% of totalWeek setWeek Due
Examination2 hour written examinationLO1, LO2, LO3, LO4, LO5100%N/AN/A

Reassessment Details

 Examination (2 hours, 100%) 

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by: 33 hours
Lecture33
Independent study (outside scheduled contact hours), broken down by:83 hours
Preparation for classes and review of material (including preparation for examination, if applicable65
completion of assessments (including examination, if applicable)18
Total Hours0 hours

Recommended Reading List

Dobson, A. J., and A. G. Barnett. 2008. An Introduction to Generalized Linear Models. CRC Press, Third Edition. 

Myers, R. H., D. C. Montgomery, G. G. Vining, and T. J. Robinson. 2010. Generalized Linear Models with Applications in Engineering and the Sciences. Wiley, 2nd edition. 

Pawitan, Yudi. 2001. In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford Science Publications. 

Tanner, M. A. 1996. Tools for Statistical Inference- Methods for the Exploration of Posterior Distributions and Likelihood Functions. Springer, 3rd Edition. 

Module Pre-requisites

Prerequisite modules: STU23501, STU22005

Module Co-requisites

None

Module Website

Blackboard