Dependent Wild Bootstrap for the Empirical Process

Autor:	Paul Doukhan, Gabriel Lang, Michael H. Neumann, Anne Leucht
Rok vydání:	2015
Předmět:	Statistics and Probability Applied Mathematics Kolmogorov–Smirnov test Confidence interval symbols.namesake Consistency (statistics) Statistics symbols Applied mathematics Statistics Probability and Uncertainty Empirical process Statistical hypothesis testing Mathematics Quantile Block (data storage)
Zdroj:	Journal of Time Series Analysis. 36:290-314
ISSN:	0143-9782
Popis:	In this paper, we propose a model-free bootstrap method for the empirical process under absolute regularity. More precisely, consistency of an adapted version of the so-called dependent wild bootstrap, that was introduced by Shao (2010) and is very easy to implement, is proved under minimal conditions on the tuning parameter of the procedure. We apply our results to construct confidence intervals for unknown parameters and to approximate critical values for statistical tests. A simulation study shows that our method is competitive to standard block bootstrap methods in finite samples.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ab8080d4549d9af04a296351c1a4af63 https://doi.org/10.1111/jtsa.12106 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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