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Software (mostly R, but Mathematica as well) in finance and insurance. Modeling financial, economic, and
insurance processes. Practical exercises and problems from finance and insurance, model testing, parameter
estimation, prediction in stochastic models and their diagnostics. Computational intensive methods, copulae, and
their application in finance and insurance. Practice with databases. Requirements: Basics of statistical modeling.
Last update: T_KPMS (14.05.2013)
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Solving real problems from finance and insurance by using stochastic models and statistical software. Last update: T_KPMS (14.05.2013)
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[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. Last update: T_KPMS (14.05.2013)
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Lecture. Last update: T_KPMS (14.05.2013)
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Software usage for the following applied methods in finance and insurance: generalized linear models, categorical data analysis, random effects models, panel/longitudinal data analysis, generalized estimating equations, bayes methods, bootstrap, Markov chain Monte Carlo, copulae, extreme observations and large claims analysis (GEV and POT methods). Last update: T_KPMS (14.05.2013)
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