| Abstract [eng] |
This paper is the asymptotic analysis of stochastic minima. Proofs of minima limit theorems are provided for cases, when linear normalization does not give non-degenerate limit distributions. In this cases, non-linear minima normalization is used. For a specific distribution, non-linear normalization functions are calculated, which are then used to get classic limit distributions for minima. Objectives: • Examine the necessity of non-linear normalization; • Analyze the possibilities for non-linear normalization in minimum pattern. Tasks: • Choose non-linear normalization function for a specific distribution; • Get classic limit distributions, where minima are normalized non-linearly; • Investigate the rate of convergence within the limit theorems; • Perform computer-based analysis of approximation errors. |
