Methods of semicontinuous data analysis (not only) in proteomics

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Data with clumping at a single value commonly occur in biometrics. Typically, the outcome variable measures an amount that must be non-negative and may in some cases be zero. Semicontinuous data can be viewed as arising from two distinct stochastic processes: one governing the occurrence of zeros and the second determining the observed value condition being a non‐zero response. The aim of the thesis is to describe and compare methods of analyzing semicontinuous data with regards to the application on statistical problems solved at IMTM, e.g. proteomics analyses.