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Relevant bibliographies by topics / Moving block bootstrap

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Academic literature on the topic 'Moving block bootstrap'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Moving block bootstrap"

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Calhoun, Gray. "BLOCK BOOTSTRAP CONSISTENCY UNDER WEAK ASSUMPTIONS." Econometric Theory 34, no. 6 (February 1, 2018): 1383–406. http://dx.doi.org/10.1017/s0266466617000500.

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This paper weakens the size and moment conditions needed for typical block bootstrap methods (i.e., the moving blocks, circular blocks, and stationary bootstraps) to be valid for the sample mean of Near-Epoch-Dependent (NED) functions of mixing processes; they are consistent under the weakest conditions that ensure the original NED process obeys a central limit theorem (CLT), established by De Jong (1997, Econometric Theory 13(3), 353–367). In doing so, this paper extends De Jong’s method of proof, a blocking argument, to hold with random and unequal block lengths. This paper also proves that bootstrapped partial sums satisfy a functional CLT (FCLT) under the same conditions.

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Ju, Hyunsu. "Moving Block Bootstrap for Analyzing Longitudinal Data." Communications in Statistics - Theory and Methods 44, no. 6 (June 20, 2013): 1130–42. http://dx.doi.org/10.1080/03610926.2013.766341.

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Park, Jinsoo, Haneul Lee, and Yun Bae Kim. "Bootstrap generated confidence interval for time averaged measure." International Journal of Modeling, Simulation, and Scientific Computing 06, no. 03 (September 2015): 1550030. http://dx.doi.org/10.1142/s1793962315500300.

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In the simulation output analysis, there are some measures that should be calculated by time average concept such as the mean queue length. Especially, the confidence interval of those measures might be required for statistical analysis. In this situation, the traditional method that utilizes the central limit theorem (CLT) is inapplicable if the output data set has autocorrelation structure. The bootstrap is one of the most suitable methods which can reflect the autocorrelated phenomena in statistical analysis. Therefore, the confidence interval for a time averaged measure having autocorrelation structure can also be calculated by the bootstrap methods. This study introduces the method that constructs these confidence intervals applying the bootstraps. The bootstraps proposed are the threshold bootstrap (TB), the moving block bootstrap (MBB) and stationary bootstrap (SB). Finally, some numerical examples will be provided for verification.

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Meinrath, Günther. "Robust spectral analysis by moving block bootstrap designs." Analytica Chimica Acta 415, no. 1-2 (June 2000): 105–15. http://dx.doi.org/10.1016/s0003-2670(00)00850-3.

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Lahiri, S. N. "On the moving block bootstrap under long range dependence." Statistics & Probability Letters 18, no. 5 (December 1993): 405–13. http://dx.doi.org/10.1016/0167-7152(93)90035-h.

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Gonçalves, Sílvia. "THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS." Econometric Theory 27, no. 5 (March 25, 2011): 1048–82. http://dx.doi.org/10.1017/s0266466610000630.

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In this paper we propose a bootstrap method for panel data linear regression models with individual fixed effects. The method consists of applying the standard moving blocks bootstrap of Künsch (1989, Annals of Statistics 17, 1217–1241) and Liu and Singh (1992, in R. LePage & L. Billiard (eds.), Exploring the Limits of the Bootstrap) to the vector containing all the individual observations at each point in time. We show that this bootstrap is robust to serial and cross-sectional dependence of unknown form under the assumption that n (the cross-sectional dimension) is an arbitrary nondecreasing function of T (the time series dimension), where T → ∞, thus allowing for the possibility that both n and T diverge to infinity. The time series dependence is assumed to be weak (of the mixing type), but we allow the cross-sectional dependence to be either strong or weak (including the case where it is absent). Under appropriate conditions, we show that the fixed effects estimator (and also its bootstrap analogue) has a convergence rate that depends on the degree of cross-section dependence in the panel. Despite this, the same studentized test statistics can be computed without reference to the degree of cross-section dependence. Our simulation results show that the moving blocks bootstrap percentile-t intervals have very good coverage properties even when the degree of serial and cross-sectional correlation is large, provided the block size is appropriately chosen.

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Gonçalves, Sílvia, and Halbert White. "THE BOOTSTRAP OF THE MEAN FOR DEPENDENT HETEROGENEOUS ARRAYS." Econometric Theory 18, no. 6 (September 24, 2002): 1367–84. http://dx.doi.org/10.1017/s0266466602186051.

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Presently, conditions ensuring the validity of bootstrap methods for the sample mean of (possibly heterogeneous) near epoch dependent (NED) functions of mixing processes are unknown. Here we establish the validity of the bootstrap in this context, extending the applicability of bootstrap methods to a class of processes broadly relevant for applications in economics and finance. Our results apply to two block bootstrap methods: the moving blocks bootstrap of Künsch (1989, Annals of Statistics 17, 1217–1241) and Liu and Singh (1992, in R. LePage & L. Billiard (eds.), Exploring the Limits of the Bootstrap, 224–248) and the stationary bootstrap of Politis and Romano (1994a, Journal of the American Statistical Association 89, 1303–1313). In particular, the consistency of the bootstrap variance estimator for the sample mean is shown to be robust against heteroskedasticity and dependence of unknown form. The first-order asymptotic validity of the bootstrap approximation to the actual distribution of the sample mean is also established in this heterogeneous NED context.

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Lahiri, S. N. "ON THE JACKKNIFE-AFTER-BOOTSTRAP METHOD FOR DEPENDENT DATA AND ITS CONSISTENCY PROPERTIES." Econometric Theory 18, no. 1 (February 2002): 79–98. http://dx.doi.org/10.1017/s0266466602181059.

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Motivated by Efron (1992, Journal of the Royal Statistical Society, Series B 54, 83–111), this paper proposes a version of the moving block jackknife as a method of estimating standard errors of block-bootstrap estimators under dependence. As in the case of independent and identically distributed (i.i.d.) observations, the proposed method merely regroups the values of a statistic from different bootstrap replicates to produce an estimate of its standard error. Consistency of the resulting jackknife standard error estimator is proved for block-bootstrap estimators of the bias and the variance of a large class of statistics. Consistency of Efron's method is also established in similar problems for i.i.d. data.

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Athreya, Krishna B., Jun-ichiro Fukuchi, and Soumendra N. Lahiri. "On the bootstrap and the moving block bootstrap for the maximum of a stationary process." Journal of Statistical Planning and Inference 76, no. 1-2 (February 1999): 1–17. http://dx.doi.org/10.1016/s0378-3758(98)00140-2.

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Srinivas, V. V., and K. Srinivasan. "Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows." Journal of Hydrology 302, no. 1-4 (February 2005): 307–30. http://dx.doi.org/10.1016/j.jhydrol.2004.07.011.

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Dissertations / Theses on the topic "Moving block bootstrap"

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Bergström, Gustav. "The Use of Importance Sampling in Bootstrap Simulations and in Moving Block Bootstrap Simulations for Efficient VaR Estimations." Thesis, Umeå universitet, Institutionen för fysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-66587.

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Almeida, Ricardo Jorge da Graça Rodrigues de. "Analysis of portfolio insurance strategies based upon empirical densities." Master's thesis, Instituto Superior de Economia e Gestão, 2012. http://hdl.handle.net/10400.5/10362.

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Mestrado em Finanças
Este estudo avalia a performance das mais comuns estratégias de Portfolio Insurance, baseando essa análise em simulações de blocos móveis de Bootstrap. Nesta análise consideramos não apenas as tradicionais medidas associadas à Teoria Média-variância, mas também outras medidas associadas ao Downside Risk, bem como classificações de dominância estocástica. Foram identificadas evidências que suportam que a estratégia CPPI 1 deve ser preferida em termos da sua dominância face às restantes. Contrariamente, a estratégia SLPI deverá ser preterida face a outras estratégias de Portfolio Insurance. Encontrámos igualmente evidências de que deverão ser escolhidas barreiras mínimas mais elevadas, com o objectivo de maximizar a utilidade da generalidade dos investidores. Consistentemente, e meramente em termos de performance, a estratégia CPPI 3 é aquela que apresenta resultados mais satisfatórios. Ao longo desta análise, tentamos proporcionar uma nova visão sobre as controversas estratégias de Portfolio Insurance, tentando tornar mais eficiente a decisão de futuros investidores.
This study evaluates the performance of the most common Portfolio Insurance Strategies based on a block-moving bootstrap simulation. We consider not only the traditional mean-variance approach, but also some measures of downside risk and stochastic dominance. We find that CPPI 1 should be preferred in terms of stochastic dominance. We also find that SLPI is constantly dominated by all the other strategies and a floor of 100% should be preferred to lower ones. Consistently, and purely in terms of performance analysis, CPPI 3 tends to outperform other strategies. During this analysis, we try to provide another insight into the controversy over Portfolio Insurance strategies, turning the decision-making process for future investors more efficient.

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Lu, Nan. "La modélisation de l'indice CAC 40 avec le modèle basé agents." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC0004/document.

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Nous développons un modèle basé agents pour reproduire deux anomalies fréquemment observées sur les marchés financiers : distribution leptokurtique des rendements et ampleur de la volatilité irrégulière mais persistante de ces mêmes rendements. Notre but est de montrer de façon probante que ces anomalies pourraient être attribuées à une formation mimétique des anticipations des intervenants sur les marchés. Nous nous éloignons des développements récents dans le domaine des modèles modèles basés agents en finance pour proposer un modèle très simple, estimé à partir des traits statistiques saillants de l’indice français journalier CAC 40. L’hypothèse d’anticipations mimétiques peut ainsi être testée : elle n’est pas rejetée dans notre modélisation
We develop an agent-based model to replicate two frequently observed anomalies in the financial markets: the fat tails and the clustered volatility of the distribution of the returns. Our goal is to show conclusively that these anomalies could be attributed to a mimetic formation of the expectations of the stakeholders in the markets. We did not follow the rencent developpments in the field of the ACE model in the finance, but we propose a very simple model which is estimated from the stylized facts of the French daily index CAC 40. The hypothesis of mimetic anticipations can thus be tested: it is not rejected in our modeling

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Zaman, Saad. "Application of Block Sieve Bootstrap to Change-Point detection in time series." Thesis, 2010. http://hdl.handle.net/10012/5456.

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Since the introduction of CUSUM statistic by E.S. Page (1951), detection of change or a structural break in time series has gained significant interest as its applications span across various disciplines including economics, industrial applications, and environmental data sets. However, many of the early suggested statistics, such as CUSUM or MOSUM, lose their effectiveness when applied to time series data. Either the size or power of the test statistic gets distorted, especially for higher order autoregressive moving average processes. We use the test statistic from Gombay and Serban (2009) for detecting change in the mean of an autoregressive process and show how the application of sieve bootstrap to the time series data can improve the performance of our test to detect change. The effectiveness of the proposed method is illustrated by applying it to economic data sets.

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Book chapters on the topic "Moving block bootstrap"

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Giordano, Francesco, Michele La Rocca, and Cira Perna. "Neural Networks and Bootstrap Methods for Regression Models with Dependent Errors." In Intelligent Data Analysis, 272–85. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-982-3.ch016.

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This chapter introduces the use of the bootstrap in a nonlinear, nonparametric regression framework with dependent errors. The aim is to construct approximate confidence intervals for the regression function which is estimated by using a single hidden layer feedforward neural network. In this framework, the use of a standard residual bootstrap scheme is not appropriate and it may lead to results that are not consistent. As an alternative solution, we investigate the AR-Sieve bootstrap and the Moving Block bootstrap, which are used to generate bootstrap replicates with a proper dependence structure. Both approaches are nonparametric bootstrap schemes, a consistent choice when dealing with neural network models which are often used as an accurate nonparametric estimation and prediction tool. In this context, both procedures may lead to satisfactory results but the AR sieve bootstrap seems to outperform the moving block bootstrap delivering confidence intervals with coverages closer to the nominal levels.

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Conference papers on the topic "Moving block bootstrap"

1

Usaola, Julio. "Synthesis of hourly wind power series using the Moving Block Bootstrap method." In 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2014. http://dx.doi.org/10.1109/pmaps.2014.6960602.

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Boufidi, Elissavet, Sergio Lavagnoli, and Fabrizio Fontaneto. "A Probabilistic Uncertainty Estimation Method for Turbulence Parameters Measured by Hot Wire Anemometry in Short Duration Wind Tunnels." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90461.

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Abstract A robust and complete uncertainty estimation method is developed to quantify the uncertainty of turbulence quantities measured by hot-wire anemometry at the inlet of a short-duration turbine test rig. The uncertainty is categorized into two macro-uncertainty sources: the measurement related uncertainty (the uncertainty of each instantaneous velocity sample) and the uncertainty stemming from the statistical treatment of the time series. The former is addressed by the implementation of a Monte Carlo method. The latter, which is directly related to the duration of the acquired signal, is estimated using the moving block bootstrap method, a non-parametric resampling algorithm suitable for correlated time series. This methodology allows computing the confidence intervals of the spanwise distributions of mean velocity, turbulence intensity, length scales and other statistical moments at the inlet of the turbine test section.

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