Bootstrapping not independent and not identically distributed data

Abstrakt

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 not independent and not identically distributed data will be presented together with theoretical justification of the proposed procedures and an example of application to data in insurance.