Module ST3454: Stochastic Models in Space and Time II
- Credit weighting (ECTS)
- 5 credits
- Semester/term taught
- Hilary term 2012-13
- Contact Hours
- 11 weeks, 3 lectures including tutorials per week
- Lecturer
- Associate Professor Rozenn Dahyot
- Learning Outcomes
- On successful completion of this module students should be able to
- Define and describe the different methods introduced in the course;
- Program, analyse and select the best model;
- Interpret output of data analysis performed by a computer statistics package;
- Module Content
-
- Kalman Filter;
- State Space Models;
- Brownian motion;
- Orntein-Uhlenbeck Process;
- Dirac function;
- Kriging;
- Spectral analysis;
- Radon transform;
- Functional data analysis (Kernel density estimates, Nadaraya-Watson estimator)
- Module Prerequisite
- Solid knowledge in mathematics and statistics required e.g. on Linear algebra, Integration and differentiation, expectation operator.
- Recommended Reading
-
- Linear Models for Multivariate, Time Series, and Spatial Data, R.Christensen, Springer 1991;
- Multivariate Geostatistics - An Introduction with Applications, H.Wackernagel, Springer 2003
- Statistics of Spatial Data, Noel Cressie, Wiley 1993
- Geostatistics for Environmental Scientists, R. Webster and M.A. Oliver, John Wiley and Sons 2001
- Time series Analysis with Applications in R, J.D. Cryer and K.S. Chan, Springer 2008
- Functional Data Analysis, J.O. Ramsay and B.W. Silverman, Springer 2006
Assessment Detail
This module will be examined jointly with ST3453 in a 3-hour examination in Trinity term, except that those taking just one of the two modules will have a 2 hour examination.