Doctoral thesis is focused on the unconventional methods of the discrete probability estimation of categorical quantity from its observed values. The gradient of quasinorm and so-called line estimation were emlopyed for these estimations. Bootstrap method was used for the improvement of accuracy. Theoretical results for selected quasinorms were illustrated on specific examples.

Master's thesis is focused on solution of the statistical problem to find a probability distribution of a discrete random variable on the basis of the observed data. These estimates are obtained by minimizing pseudo-quasinorm which is introduced here.The thesis further focuses on atributes of this pseudo-quasinorm. It also contains practical application of these methods.

Dissertation thesis is focused on solution of the statistical problem to find a probability distribution of a discrete random variable on the basis of the observed data. These estimates are obtained by minimizing quasi-norms with given constraints. The thesis further focuses on deriving confidence intervals for estimated probabilities. It also contains practical application of these methods.