## Advanced Regression Analysis
Course category:
Masters
Module code:
MAT00042M
Year:
2012/13
Term:
Autumn
Credits:
20
Lecturer:
Professor Wenyang Zhang
## AimsThis module is to teach students how to derive, from first principles and using matrix algebra, theoretical results relating to fitting regression models by least squares, local least squares or maximum likelihood approach, how to select a regression model to fit a given data set and carry out related statistical inferences using appropriate computer software. ## Learning objectivesAt the end of the module you should -
have a reasonable ability to derive theoretical results relating to fitting regression models. -
have a reasonable ability to fit regression models to data, and carry out related statistical inferences using appropriate computer software. -
have a reasonable ability to use residual plots and other techniques to check the assumptions underlying regression analysis. -
have a reasonable ability to choose between alternative models for sets of data.
## Syllabus- Exponential family and generalised linear models; Estimation (ML) and inference; Model selection; Model checking;
- Nonparametric regression models; Local linear modelling; Local least square estimation; Bandwidth selection; Varying-coefficient models.
## Recommended textsN. R. Draper and H. Smith, S. Chatterjee and B. Price P. McCullagh, J . Nelder, Fan, J. and Gijbels, I. ## Teaching- Autumn Term
- 3 lectures per week
- 1 computer practical or problems class per week
## AssessmentTwo hours closed examination week 1 Spring Term 90% ## Elective informationThis module is not available as an elective. ## Prerequisites |
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