|
|
|
||
Machine learning + statistical inference = statistical learning. Methods of statistical learning for categorical
(unordered and ordered), nominal (discrete and continuous), truncated, censored, extremal, and clustered data.
Modeling (not only) financial, economic, and insurance processes. Practical exercises and problems from
econometrics, model testing, parameter estimation, prediction in stochastic models and their diagnostics.
Computational demanding stochastic techniques.
Last update: Branda Martin, doc. RNDr., Ph.D. (05.12.2020)
|
|
||
[1] Peter Dalgaard: Introductory statistics with R. Birkhäuser, 2002. [2] David W. Hosmer and Stanley Lemeshow: Applied Logistic Regression (Second edition). Wiley, 2000. [3] John H. Maindonald, John Braun: Data analysis and graphics using R: an example-based approach (Third edition). Cambridge University Press, 2010. [4] David Ruppert: Statistics and finance: An introduction. Springer, 2004. [5] William N. Venables, Brian D. Ripley: Modern applied statistics with S. Birkhäuser, 2002. [6] Gareth James, Diana Witten, Trevor Hastie, and Robert Tibshirani: An Introduction to Statistical Learning (with Applications in R). Springer, 2013. [7] Christian Bluhm, Ludger Overbeck, and Christoph Wagner: Introduction to Credit Risk Modeling (Second edition). Chapman & Hall, 2010. Last update: Pešta Michal, doc. RNDr., Ph.D. (05.12.2020)
|
|
||
Lecture. Last update: Zichová Jitka, RNDr., Dr. (03.06.2022)
|
|
||
1. Robust regression. 2. Logistic regression. 3. Multinomial regression. 4. Poisson regression. 5. Truncated and inflated data. 6. Survival analysis. 7. Analysis of panel/longitudinal data. Last update: Pešta Michal, doc. RNDr., Ph.D. (05.12.2020)
|