Title: Bootstrapping not independent and not identically distributed data Authors:  Martin Hrba - Charles University (Czech Republic) [presenting]
Matus Maciak - Charles University (Czech Republic)
Barbora Pestova - Charles University (Czech Republic)
Michal Pesta - Charles University (Czech Republic)
Abstract: Classical normal asymptotics could bring serious pitfalls in statistical inference because some parameters appearing in the limit distributions are unknown and, moreover, complicated to estimate (from a theoretical as well as computational point of view). Due to this, plenty of stochastic approaches for constructing confidence intervals and testing hypotheses cannot be directly applied. Bootstrap seems to be a plausible alternative. A methodological framework for bootstrapping non-independent and not identically distributed data is presented together with a theoretical justification of the proposed procedures. Among others, bootstrap laws of large numbers and central limit theorems are provided.