Bootstrapping Not Independent and Not Identically Distributed Data

0567128 - ÚI 2023 RIV CH eng J - Journal Article
Hrba, M. - Maciak, M. - Peštová, Barbora - Pešta, M.
Bootstrapping Not Independent and Not Identically Distributed Data.
Mathematics. Roč. 10, č. 24 (2022), č. článku 4671. E-ISSN 2227-7390
R&D Projects: GA ČR(CZ) GA21-03658S
Institutional support: RVO:67985807
Keywords : bootstrap * statistical inference * asymptotic normality * weakly dependent data * not identically distributed data * moving block bootstrap * law of large numbers * central limit theorem * psychometric evaluation * non-life insurance
OECD category: Statistics and probability
Impact factor: 2.4, year: 2022
Method of publishing: Open access
https://dx.doi.org/10.3390/math10244671

Classical normal asymptotics could bring serious pitfalls in statistical inference, because some parameters appearing in the limit distributions are unknown and, moreover, complicated to estimated (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 is presented together with theoretical justification of the proposed procedures. Among others, bootstrap laws of large numbers and central limit theorems are provided. The developed methods are utilized in insurance and psychometry.
Permanent Link: https://hdl.handle.net/11104/0338390