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Bootstrapping Not Independent and Not Identically Distributed Data
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SYSNO ASEP 0567128 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Bootstrapping Not Independent and Not Identically Distributed Data Author(s) Hrba, M. (CZ)
Maciak, M. (CZ)
Peštová, Barbora (UIVT-O) RID, SAI
Pešta, M. (CZ)Article number 4671 Source Title Mathematics. - : MDPI
Roč. 10, č. 24 (2022)Number of pages 26 s. Language eng - English Country CH - Switzerland 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 R&D Projects GA21-03658S GA ČR - Czech Science Foundation (CSF) Method of publishing Open access Institutional support UIVT-O - RVO:67985807 UT WOS 000902904500001 EID SCOPUS 85144741403 DOI 10.3390/math10244671 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2023 Electronic address https://dx.doi.org/10.3390/math10244671
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