Generalized Linear Models (8 ECTS)

Course Code: 
Compulsory Courses

GGM Theory: Covariance matrix and the Wald test. Maximum likelihood estimation: scores and its distribution, asymptotic distribution of the maximum likelihood estimators and the likelihood ratio. The exponential distributions family. Generalised linear model likelihood analysis, maximum likelihood estimation in the generalised linear model: scores, the Fisher information and the Newton-Raphson algorithm. Relation to weighted least squares. Inference for coefficients. Deviation from the saturated model. Models with an unknown F. Residuals. Applications, examples: binomial data: Link functions, coefficients interpretation, inference, overdispersion. One factor analysis (categorical or continuous), two or more factors analysis, with or without interactions: parameterisations, design matrices, coefficients interpretation. Probit and clog-log models examples. Poisson and log-linear models. Contingency tables, odds ratio and log-linear parameters. Multinomial and multinomial product, equivalency with log-linear, log-linear and logistic regression. Independence, group independence, conditional independence, uniform dependence. Overdispersion, overdispersion test and dispersion index, the negative binomial model and other alternatives.

    Recommended Reading

  • Agresti, A. (2015), Foundations of Linear and Generalized Linear Models, Wiley Series in Probability and Statistics         
  • Agresti, A. (2012), Categorical Data Analysis, 3rd edition, Wiley Series in Probability and Statistics
  • Dobson & Barnett (2008), An Introduction to Generalized Linear Models, Taylor & Francis.
  • Fox  (2008), Applied Regression Analysis and Generalized Linear Models, Kindle
  • Hosmer, D.W. and Lemeshow, S. (1989, 2000), Applied Logistic Regression. New York: Wiley.
  • McGullagh, P and Nelder, J.A. (1989), Generalized Linear Models, London: Chapman and Hall.