Timing
4 hours weekly (3+1) during Winter Terms.
Lecturer
Herwig Friedl .
Teaching Aims
To give more emphasis on statistical modelling. Starting from the linear
regression framework, results and techniques in the field of generalized
linear models will be developed. To improve the student's ability to apply
the theory in exploratory data analysis and further in statistical modelling.
To introduce statistical software packages like R.
Learning Objectives
Students should be able to analyse real data problems by their own. They
should be also familiar with the concepts of exploratory data analysis and
should find and verify relationships in the data by applying the ideas of
statistical modelling.
Contents
(0) Linear Regression Models.
(1) Box-Cox Transformation Family.
(2) Exponential Family: Members, Maximum-Likelihood Estimates (Score,
Information), Quasi-Likelihood Technique.
(3) Generalized Linear Models: Parameterization, Maximum-Likelihood
Estimation, Pearsons' Chi-Square Statistic, Deviance and
Quasi-Deviance, Modelling Over- (Under-) Dispersion.
(4) Logistic Regression: Binomial Models, Logit-Models, Log Odds Ratio.
(5) Loglinear Models: Poisson Models, Multinomial Responses, Contingency
Tables.
(6) Random Effect Models: EM (Expectation/Maximization) Algorithm,
Mixture Models, Gauss-Hermite Quadrature (Approximation),
Nonparametric Maximum-Likelihood Estimation, Standard Errors.
Pre-requisites
Probability, Mathematical Statistics, Regression Analysis.
Literature
Download Homeworks:
Download Datasets:
Download R Macros and R Statements useful to analyze the data: