Computationally Intensive Methods
Module code: MD7443
Module co-ordinator: Professor John Thompson
- An introduction to the Likelihood analysis of a statistical model with several unknown parameters, considers the more controversial topic of hypothesis testing based on the likelihood and extends the use of maximum likelihood to more complex problems and looks at the statistics that can be derived from the results.
- How to apply splines and fractional polynomials when fitting non-linear functions including how to choose between models and plot the fitted functions.
- The ideas of bootstrapping and to be able to obtain confidence intervals for parameters in situations where it is difficult to obtain confidence intervals analytically.
- Missing data patterns, problems arising from missing data and the basic concepts of multiple imputation.
- The use of simulation in statistics and in particular for assessing the performance of statistical methods in terms of bias and convergence. In addition the use of simulation for complex power calculations.
Bayes and MCMC
- Bayesian Methods for multi-parameter problems
- Introduction to WinBUGS
- Simple analyses of clinical trials
- Hierarchical Normal Models
- Priors for Variances
- Measurement Error
- Missing Data/Prediction
- Multivariate Models
- Repeated Measures
- Other models: Logistic regression & Meta-Analysis
- 20 one-hour lectures
- 24 one-hour workshops
- Coursework 1 (50%)
- Coursework 2 (50%)
Participation in the Fundamentals and Statistical Modelling modules.