Dr Matteo Barigozzi COL 7.11

This course is available on the MSc in Econometrics and Mathematical Economics, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

Students must have completed Mathematical Methods (MA100) and Probability, Distribution Theory and Inference (ST202).

Generalized linear modelling with an emphasis on diagnostics, estimation, and inference. Variables belonging to the exponential family. Survival analysis. Linear regressions. Variable selection and model building. Deletion diagnostics. Analysis of variance. Transformation of the response, constructed variables. Maximum likelihood estimation. Exponential family and generalized linear models. Categorical data, binary variables and logistic regressions. Log-linear models and contingency tables. Exploratory analysis of survivor distributions and hazard rates. Regression modelling for survival data. The use of R for data analysis.

20 hours of lectures and 10 hours of seminars in the LT.

A C Atkinson & M Riani, Robust Diagnostic Regression Analysis;  A Dobson & A Barnett, An Introduction to Generalised Linear Modelling; P McCullagh & J A Nelder, Generalized Linear Models; A Agresti, Categorical Data Analysis;  R Venables & D M Smith, An Introduction to R (downloadable). D. W. Hosmer & S. Lemeshow & S. May, Applied Survival Analysis, Regression Modeling of Time-to-Event Data.

Exam (100%, duration: 2 hours) in the main exam period.