This module will be offered again in the 2021/2022 Academic Year.
|Module Name||Applied Linear Statistical Methods II|
|ECTS Weighting ||5 ECTS|
|Semester taught||Semester 2|
|Module Coordinator/s||Alessio Benavoli|
Module Learning Outcomes
On successful completion of this module, students will be able to:
- Demonstrate ways in which the multivariate linear regression model can be generalised to non-linear and non-Gaussian cases;
- Define the generalised linear model and implement an analysis with specific examples of this model;
- Motivate the use of deviance as a measure of model fit and its use in estimating prediction error;
- Define the Kalman Filter and derive the updating equations from Bayes Law and properties of the multivariate Gaussian distribution.
- Apply the Kalman Filter to object tracking and trading.
The topics covered are:
- Recap of linear regression
- The exponential family of distributions
- The generalised linear model
- Specific examples: binomial, Poisson, logistic
- Applications and examples
- R programming
Teaching and learning Methods
|Assessment Component||Brief Description||Learning Outcomes Addressed||% of total||Week set||Week Due|
|Examination||2 hour written examination||LO1, LO2, LO3, LO4, LO5||100%||N/A||N/A|
Examination (2 hours, 100%)
Contact Hours and Indicative Student Workload
|Contact Hours (scheduled hours per student over full module), broken down by:||33 hours|
|Independent study (outside scheduled contact hours), broken down by:||83 hours|
|Preparation for classes and review of material (including preparation for examination, if applicable||65|
|completion of assessments (including examination, if applicable)||18|
|Total Hours||0 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.
Prerequisite modules: STU23501, STU22005