The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
The Stationary Bootstrap
Abstract
This article introduces a resampling procedure called the stationary bootstrap as a means of calculating standard errors of estimators and constructing confidence regions for parameters based on weakly dependent stationary observations. Previously, a technique based on resampling blocks of consecutive observations was introduced to construct confidence intervals for a parameter of the m-dimensional joint distribution of m consecutive observations, where m is fixed. This procedure has been generalized by constructing a “blocks of blocks” resampling scheme that yields asymptotically valid procedures even for a multivariate parameter of the whole (i.e., infinite-dimensional) joint distribution of the stationary sequence of observations. These methods share the construction of resampling blocks of observations to form a pseudo-time series, so that the statistic of interest may be recalculated based on the resampled data set. But in the context of applying this method to stationary data, it is natural to require the resampled pseudo-time series to be stationary (conditional on the original data) as well. Although the aforementioned procedures lack this property, the stationary procedure developed here is indeed stationary and possesses other desirable properties. The stationary procedure is based on resampling blocks of random length, where the length of each block has a geometric distribution. In this article, fundamental consistency and weak convergence properties of the stationary resampling scheme are developed.
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“We resample G using the stationary bootstrap method of Politis and Romano (1994), with prespecified parameter set X, for B times, and label each resample as G b , b = 1, ⋯ , B.”
From Profitability and Data-Snooping Tests of Four Technical Trade Strategies for Cryptocurrency Pair BTC/USDT and ETH/USDT in Cryptocurrency Markets During 2022–2023 (2023) · cites this paper as Politis and Romano (1994)
Citations
Cites
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
Approximation Theorems of Mathematical Statistics
Serfling, R. J. (1980) · Wiley series in probability and statistics · doi:10.1002/9780470316481 · Cited by 5,908
Spectral Analysis and Time Series
Preston, D. B., & Priestley, M. B. (1983) · Technometrics · doi:10.1080/00401706.1983.10487866 · Cited by 5,050
Martingale Limit Theory and its Application.
Loynes, R. M., Hall, P. V., & Heyde, C. C. (1984) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2981599 · Cited by 2,746
A Course in Probability Theory
(1974) · Elsevier eBooks · doi:10.1016/c2009-0-22097-6 · Cited by 2,735
Time Series: Data Analysis and Theory.
Koopmans, L. H., & Brillinger, D. R. (1976) · Journal of the American Statistical Association · doi:10.2307/2285347 · Cited by 2,647
The Jackknife and the Bootstrap for General Stationary Observations
Künsch, H. R. (1989) · The Annals of Statistics · doi:10.1214/aos/1176347265 · Cited by 2,454
Martingale Limit Theory and its Application
(1980) · Elsevier eBooks · doi:10.1016/c2013-0-10818-5 · Cited by 2,073
On the Theoretical Specification and Sampling Properties of Autocorrelated Time-Series
Bartlett, M. S. (1946) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.2307/2983611 · Cited by 916
Time Series: Data Analysis and Theory.
Dickey, D. A., & Brillinger, D. R. (1982) · Journal of the American Statistical Association · doi:10.2307/2287796 · Cited by 696
Research works printed but not linked: 4
- 10.1080/00401706.1992.10484950 · DOI
- Liu R. Y., 1992, Exploring the Limits of Bootstrap
- Politis D., Computing Science and Statistics, Proceedings of the 22nd Symposium on the Interface, 98
- Politis D., 1992, Exploring the Limits of Bootstrap, 263
Cited by
Sorted by citations
Hansen, P. R., Lunde, A., & Nason, J. M. (2011) · Econometrica · doi:10.3982/ecta5771 · Cited by 2,229
A Reality Check for Data Snooping
White, H. (2000) · Econometrica · doi:10.1111/1468-0262.00152 · Cited by 1,851
Output Fluctuations in the United States: What Has Changed since the Early 1980's?
Pérez‐Quirós, G., & McConnell, M. M. (2000) · RePEc: Research Papers in Economics · doi:10.1257/aer.90.5.1464 · Cited by 1,869
A forecast comparison of volatility models: does anything beat a GARCH(1,1)?
Hansen, P. R., & Lunde, A. (2005) · Journal of Applied Econometrics · doi:10.1002/jae.800 · Cited by 1,879
Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns
Cappiello, L., Engle, R. F., & Sheppard, K. (2006) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbl005 · Cited by 1,777
Metadata
- Authors
- Politis, D. N., & Romano, J. P.
- Affiliations
- Purdue University West Lafayette (US)
- Stanford University (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1994-12
- Volume
- 89
- Issue
- 428
- Pages
- 1303-1313
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2076396344
- Cited by
- 2,649
- References
- 27
- Citations by year
- 2026: 279 · 2025: 137 · 2024: 113 · 2023: 108 · 2022: 102 · 2021: 124 · 2020: 136 · 2019: 117 · 2018: 117 · 2017: 129 · 2016: 125 · 2015: 120 · 2014: 119 · 2013: 117 · 2012: 130
- FWCI
- 9.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Helmholtz Centre for Ocean Research Kiel (GEOMAR) (acceptedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Keywords
- stationary bootstrap, resampling, standard errors, confidence regions, geometric distribution, consistency, weak convergence, infinite-dimensional parameters, block bootstrap
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