Nonparametric and Robust Methods (SL_116)
About Study Course
Objective
The objective of this course is to give students the in-depth knowledge of nonparametric and robust methods in mathematical statistics. In biostatistical applications it is common that the sample sizes are small and the normality of data is questionable. Moreover, the classical t-test and ANOVA procedure require additionally homogeneity condition which is often violated. Nonparametric and robust procedures often are used in those situations. Classical linear regression also requires normality assumption and is limited to describe only the linear dependence. Nonparametric smoothing techniques allow to estimate the regression function in a very general way. Resampling methods are popular especially for deriving confidence intervals. The software package R will be used for computation and case study applications.
Prerequisites
• Familiarity with probability theory and mathematical statistics.
• Basic knowledge in R is required.
Learning outcomes
• understand knowledge of and are able to define concepts and procedures of nonparametric and robust statistical procedures.
• are acquainted with and are able to choose nonparametric and robust statistical procedures in program R.
• Perform nonparametric testing in R and interpret the results.
• Use and apply smoothing techniques for density and regression function estimation.
• Be able to perform data resampling methods.
• Apply robust procedures for different statistical data problems.
• Understand and support the importance of assumptions made in standard statistical methods.
• Be able to make justified decisions between parametric, nonparametric and robust procedures for practical data analysis, demonstrate understanding and ethical responsibility for the potential impact of scientific results on the environment and society.
• Independently develop a correct statistical model, critically interpret and present the obtained results, if necessary, further analysis will be performed
