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Academic literature on the topic 'Bootstrap'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 April 2025

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Journal articles on the topic "Bootstrap"

1

Davidson, Russell, and James G. MacKinnon. "Bootstrap tests: how many bootstraps?" Econometric Reviews 19, no. 1 (January 2000): 55–68. http://dx.doi.org/10.1080/07474930008800459.

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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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Darmon, David. "Discrete Information Dynamics with Confidence via the Computational Mechanics Bootstrap: Confidence Sets and Significance Tests for Information-Dynamic Measures." Entropy 22, no. 7 (July 17, 2020): 782. http://dx.doi.org/10.3390/e22070782.

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Information dynamics and computational mechanics provide a suite of measures for assessing the information- and computation-theoretic properties of complex systems in the absence of mechanistic models. However, both approaches lack a core set of inferential tools needed to make them more broadly useful for analyzing real-world systems, namely reliable methods for constructing confidence sets and hypothesis tests for their underlying measures. We develop the computational mechanics bootstrap, a bootstrap method for constructing confidence sets and significance tests for information-dynamic measures via confidence distributions using estimates of ϵ -machines inferred via the Causal State Splitting Reconstruction (CSSR) algorithm. Via Monte Carlo simulation, we compare the inferential properties of the computational mechanics bootstrap to a Markov model bootstrap. The computational mechanics bootstrap is shown to have desirable inferential properties for a collection of model systems and generally outperforms the Markov model bootstrap. Finally, we perform an in silico experiment to assess the computational mechanics bootstrap’s performance on a corpus of ϵ -machines derived from the activity patterns of fifteen-thousand Twitter users.

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Kern, Stefan, Anja Rösel, Leif Toudal Pedersen, Natalia Ivanova, Roberto Saldo, and Rasmus Tage Tonboe. "The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations." Cryosphere 10, no. 5 (September 26, 2016): 2217–39. http://dx.doi.org/10.5194/tc-10-2217-2016.

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Abstract. Sea-ice concentrations derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-ice surface itself. We investigate the sensitivity of eight sea-ice concentration retrieval algorithms to melt ponds by comparing sea-ice concentration with the melt-pond fraction. We derive gridded daily sea-ice concentrations from microwave brightness temperatures of summer 2009. We derive the daily fraction of melt ponds, open water between ice floes, and the ice-surface fraction from contemporary Moderate Resolution Spectroradiometer (MODIS) reflectance data. We only use grid cells where the MODIS sea-ice concentration, which is the melt-pond fraction plus the ice-surface fraction, exceeds 90 %. For one group of algorithms, e.g., Bristol and Comiso bootstrap frequency mode (Bootstrap_f), sea-ice concentrations are linearly related to the MODIS melt-pond fraction quite clearly after June. For other algorithms, e.g., Near90GHz and Comiso bootstrap polarization mode (Bootstrap_p), this relationship is weaker and develops later in summer. We attribute the variation of the sensitivity to the melt-pond fraction across the algorithms to a different sensitivity of the brightness temperatures to snow-property variations. We find an underestimation of the sea-ice concentration by between 14 % (Bootstrap_f) and 26 % (Bootstrap_p) for 100 % sea ice with a melt-pond fraction of 40 %. The underestimation reduces to 0 % for a melt-pond fraction of 20 %. In presence of real open water between ice floes, the sea-ice concentration is overestimated by between 26 % (Bootstrap_f) and 14 % (Bootstrap_p) at 60 % sea-ice concentration and by 20 % across all algorithms at 80 % sea-ice concentration. None of the algorithms investigated performs best based on our investigation of data from summer 2009. We suggest that those algorithms which are more sensitive to melt ponds could be optimized more easily because the influence of unknown snow and sea-ice surface property variations is less pronounced.

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Breivik, Øyvind, and Ole Johan Aarnes. "Efficient bootstrap estimates for tail statistics." Natural Hazards and Earth System Sciences 17, no. 3 (March 8, 2017): 357–66. http://dx.doi.org/10.5194/nhess-17-357-2017.

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Abstract. Bootstrap resamples can be used to investigate the tail of empirical distributions as well as return value estimates from the extremal behaviour of the sample. Specifically, the confidence intervals on return value estimates or bounds on in-sample tail statistics can be obtained using bootstrap techniques. However, non-parametric bootstrapping from the entire sample is expensive. It is shown here that it suffices to bootstrap from a small subset consisting of the highest entries in the sequence to make estimates that are essentially identical to bootstraps from the entire sample. Similarly, bootstrap estimates of confidence intervals of threshold return estimates are found to be well approximated by using a subset consisting of the highest entries. This has practical consequences in fields such as meteorology, oceanography and hydrology where return values are calculated from very large gridded model integrations spanning decades at high temporal resolution or from large ensembles of independent and identically distributed model fields. In such cases the computational savings are substantial.

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Manteiga, Wenceslao Gonz�lez, and Miguel A. Delgado. "bootstrap." Annals of Statistics 29, no. 5 (October 2001): 1469–507. http://dx.doi.org/10.1214/aos/1013203462.

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Hesterberg, Tim. "Bootstrap." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 6 (September 8, 2011): 497–526. http://dx.doi.org/10.1002/wics.182.

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Wang, Suojin. "On the bootstrap and smoothed bootstrap." Communications in Statistics - Theory and Methods 18, no. 11 (January 1989): 3949–62. http://dx.doi.org/10.1080/03610928908830134.

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Diciccio, Thomas, and Robert Tibshirani. "Bootstrap Confidence Intervals and Bootstrap Approximations." Journal of the American Statistical Association 82, no. 397 (March 1987): 163–70. http://dx.doi.org/10.1080/01621459.1987.10478409.

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Mammen, Enno. "Bootstrap, wild bootstrap, and asymptotic normality." Probability Theory and Related Fields 93, no. 4 (December 1992): 439–55. http://dx.doi.org/10.1007/bf01192716.

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Dissertations / Theses on the topic "Bootstrap"

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Lee, Stephen Man Sing. "Generalised bootstrap procedures." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319569.

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Kan, Hasan E. "Bootstrap based signal denoising." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://bosun.nps.edu/uhtbin/hyperion.exe/02Sep%5FKan.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2002.
Thesis Advisor(s): Monique P. Fargues, Ralph D. Hippenstiel. "September 2002." Includes bibliographical references (p. 89-90). Also available in print.

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Ip, Wai Cheung. "Bootstrap methods in econometrics." Thesis, University of Leeds, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292325.

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NETO, ANSELMO CHAVES. "BOOTSTRAP IN TIME SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1991. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8324@1.

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O bootstrap de B. Efron, que não poderia ser imaginado sem os computadores de hoje, pode resolver vÃ¡rios problemas livre da suposição de Gaussianidade para os dados. Este trabalho tem o objetivo de apresentar essa técnica computacionalmente intensiva no contexto de Séries temporais - Metodologia Box and Jenkins. Como se sabe essa Metodologia possui alguns resultados assintóticos. Então, na fase da identificação da estrutura do modelo, pode apresentar problemas em regiões do espaço paramétrico aqui determinadas,. O bootstrap é proposto como opção e um estudo de simulação, comparativo, é apresentado. Constrói- se a distribuição bootstrap da autocorrelação e autocorrelação parcial, amostrais, e ainda a distribuição bootstrap do estimador de MQNL dos coeficientes de modelos ARMA (p, q). consequentemente, fica disponível medida não- paramétrica da precisão da estimativa. O estudo de simulação que aborda o estimador de MQNL dos coeficientes enfoca, basicamente, a região de fronteira da estacionariedade e inversibilidade.
The bootstrap of B. Efron, what should not be imagined without fast andcheaper computation, can solve several problems free from assumption that the data conform to a bell-shaped curve. This work has the aim to present this computer-intensive technics in the context of Time Series - Box and Jenkins´s Methodology. As we know this methodology own some asymptotic results. Then in the identification stage of the structure of the model it may present some troubles on regions of the parametric space, as we show later on the bootstrap is proposed as an aption and a comparative simulation study is pointed out. We build up the bootstrap distribution of the sample autocorrelation and sample partial autocorrelation, and yet a bootstrap distribution to the non-linear LS estimator of the coefficients to the ARMA (p,q) model. As a consequence we get the non- parametric measure of the accuracy of the estimates. The study of simulation wich takes into account the non-linear LS estimato to the coefficients, actually focalize the borden of the stationarity and invertibility region.

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Ramli, Ahmad Lutfi Amri Bin. "Bootstrap based surface reconstruction." Thesis, Durham University, 2012. http://etheses.dur.ac.uk/4468/.

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Surface reconstruction is one of the main research areas in computer graphics. The goal is to find the best surface representation of the boundary of a real object. The typical input of a surface reconstruction algorithm is a point cloud, possibly obtained by a laser 3D scanner. The raw data from the scanner is usually noisy and contains outliers. Apart from creating models of high visual quality, assuring that a model is as faithful as possible to the original object is also one of the main aims of surface reconstruction. Most surface reconstruction algorithms proposed in the literature assess the reconstructed models either by visual inspection or, in cases where subjective manual input is not possible, by measuring the training error of the model. However, the training error underestimates systematically the test error and encourages overfitting. In this thesis, we provide a method for quantitative assessment in surface reconstruction. We integrate a model averaging method from statistics called bootstrap and define it into our context. Bootstrapping is a resampling procedure that provides statistical parameter. In surface fitting, we obtained error estimate which detect error caused by noise or bad fitting. We also define bootstrap method in context of normal estimation. We obtain variance and error estimates which we use as a quality measure of normal estimates. As application, we provide smoothing algorithm for point clouds and normal smoothing that can handle feature area. We also developed feature detection algorithm.

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Kan, Hasan Ertam. "Bootstrap based signal denoising." Thesis, Monterey, California. Naval Postgraduate School, 2002. http://hdl.handle.net/10945/2883.

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Approved for public release, distribution is unlimited
"This work accomplishes signal denoising using the Bootstrap method when the additive noise is Gaussian. The noisy signal is separated into frequency bands using the Fourier or Wavelet transform. Each frequency band is tested for Gaussianity by evaluating the kurtosis. The Bootstrap method is used to increase the reliability of the kurtosis estimate. Noise effects are minimized using a hard or soft thresholding scheme on the frequency bands that were estimated to be Gaussian. The recovered signal is obtained by applying the appropriate inverse transform to the modified frequency bands. The denoising scheme is tested using three test signals. Results show that FFT-based denoising schemes perform better than WT-based denoising schemes on the stationary sinusoidal signals, whereas WT-based schemes outperform FFT-based schemes on chirp type signals. Results also show that hard thresholding never outperforms soft thresholding, at best its performance is similar to soft thresholding."--p.i.
First Lieutenant, Turkish Army

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Rifai, Khaled. "Bootstrap-Instrumente für Unternehmensgründungen." Lohmar Eul, 2009. http://d-nb.info/995568170/04.

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Castedo, Echeverri Alejandro. "CFTs and the Bootstrap." Doctoral thesis, SISSA, 2016. http://hdl.handle.net/20.500.11767/3581.

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This thesis deals with investigations in the field of higher dimensional CFTs. The first part is focused on the technology neccessary for the calculation of general conformal blocks in 4D CFTs. These special functions are neccessary for general boostrap analysis in 4D CFTs. We show how to reduce the calculation of arbitrary conformal blocks to the calculation of a minimal set of "seed" conformal blocks through the use of differential operators. We explicitly write the set of operators necessary and show a general basis for the case of external traceless symmetric operators. We then compute in closed analytical form this set of seeds. We write in a compact form the set of quadratic Casimir equations and proceed to solve them in closed form with the use of an educated Ansatz. Various details on the form of the ansatz are deduced with the use of the so called shadow formalism. The second part of this thesis deals with numerical investigations of the bootstrap equation for external scalar operators. We compute bounds on the OPE coefficients in 4D CFTs for theories with and without global symmetries, and write the bootstrap equations for theories with SO(N ) × SO(M ) and SU (N ) × SO(M ) symmetries. The last part of the thesis presents the Multipoint bootstrap, a conformal-bootstrap method advocated in ref. [25]. In contrast to the most used method based on derivatives evaluated at the symmetric point z = z = 1/2, ̄ we can consistently “integrate out" higher-dimensional operators and get a reduced, simpler, and faster to solve, set of bootstrap equations. We test this “effective" bootstrap by studying the 3D Ising and O(n) vector models and bounds on generic 4D CFTs, for which extensive results are already available in the literature. We also determine the scaling dimensions of certain scalar operators in the O(n) vector models, with n = 2, 3, 4, which have not yet been computed using bootstrap techniques.

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Przykucki, Michał Jan. "Extremal and probabilistic bootstrap percolation." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/245349.

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In this dissertation we consider several extremal and probabilistic problems in bootstrap percolation on various families of graphs, including grids, hypercubes and trees. Bootstrap percolation is one of the simplest cellular automata. The most widely studied model is the so-called r-neighbour bootstrap percolation, in which we consider the spread of infection on a graph G according to the following deterministic rule: infected vertices of G remain infected forever and in successive rounds healthy vertices with at least r already infected neighbours become infected. Percolation is said to occur if eventually every vertex is infected. In Chapter 1 we consider a particular extremal problem in 2-neighbour bootstrap percolation on the n \times n square grid. We show that the maximum time an infection process started from an initially infected set of size n can take to infect the entire vertex set is equal to the integer nearest to (5n^2-2n)/8. In Chapter 2 we relax the condition on the size of the initially infected sets and show that the maximum time for sets of arbitrary size is 13n^2/18+O(n). In Chapter 3 we consider a similar problem, namely the maximum percolation time for 2-neighbour bootstrap percolation on the hypercube. We give an exact answer to this question showing that this time is \lfloor n^2/3 \rfloor. In Chapter 4 we consider the following probabilistic problem in bootstrap percolation: let T be an infinite tree with branching number \br(T) = b. Initially, infect every vertex of T independently with probability p > 0. Given r, define the critical probability, p_c(T,r), to be the value of p at which percolation becomes likely to occur. Answering a problem posed by Balogh, Peres and Pete, we show that if b \geq r then the value of b itself does not yield any non-trivial lower bound on p_c(T,r). In other words, for any \varepsilon > 0 there exists a tree T with branching number \br(T) = b and critical probability p_c(T,r) < \varepsilon. However, in Chapter 5 we prove that this is false if we limit ourselves to the well-studied family of Galton--Watson trees. We show that for every r \geq 2 there exists a constant c_r>0 such that if T is a Galton--Watson tree with branching number \br(T) = b \geq r then \[ p_c(T,r) > \frac{c_r}{b} e^{-\frac{b}{r-1}}. \] We also show that this bound is sharp up to a factor of O(b) by describing an explicit family of Galton--Watson trees with critical probability bounded from above by C_r e^{-\frac{b}{r-1}} for some constant C_r>0.

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Richard, Patrick. "Sieve bootstrap unit root tests." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103285.

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We consider the use of a sieve bootstrap based on moving average (MA) and autoregressive moving average (ARMA) approximations to test the unit root hypothesis when the true Data Generating Process (DGP) is a general linear process. We provide invariance principles for these bootstrap DGPs and we prove that the resulting ADF tests are asymptotically valid. Our simulations indicate that these tests sometimes outperform those based on the usual autoregressive (AR) sieve bootstrap. We study the reasons for the failure of the AR sieve bootstrap tests and propose some solutions, including a modified version of the fast double bootstrap.
We also argue that using biased estimators to build bootstrap DGPs may result in less accurate inference. Some simulations confirm this in the case of ADF tests. We show that one can use the GLS transformation matrix to obtain equations that can be used to estimate bias in general ARMA(p,q) models. We compare the resulting bias reduced estimator to a widely used bootstrap based bias corrected estimator. Our simulations indicate that the former has better finite sample properties then the latter in the case of MA models. Finally, our simulations show that using bias corrected or bias reduced estimators to build bootstrap DGP sometimes provides accuracy gains.

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Books on the topic "Bootstrap"

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DiCiccio, Thomas J. Bootstrap confidence intervals and bootstrap approximation. Toronto: University of Toronto, Dept. of Statistics, 1985.

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Dikta, Gerhard, and Marsel Scheer. Bootstrap Methods. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73480-0.

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Matsinopoulos, Panos. Practical Bootstrap. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6071-5.

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Barbe, Philippe. The weighted bootstrap. New York: Springer-Verlag, 1995.

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Krause, Jörg. Introducing Bootstrap 4. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6203-0.

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Krause, Jörg. Introducing Bootstrap 4. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2382-6.

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Barbe, Philippe, and Patrice Bertail. The Weighted Bootstrap. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2532-4.

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Bursten, Steven C. The bootstrap entrepreneur. Nashville: T. Nelson Publishers, 1993.

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Mammen, E. When does bootstrap work?: Asymptotic results and simulations. New York: Springer-Verlag, 1992.

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Mammen, Enno. When Does Bootstrap Work? New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2950-6.

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Book chapters on the topic "Bootstrap"

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Uzayr, Sufyan bin. "The Bootstrap Framework." In Bootstrap, 19–70. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-2.

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Uzayr, Sufyan bin. "Designing Templates." In Bootstrap, 71–112. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-3.

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Uzayr, Sufyan bin. "Cheat Sheet 3." In Bootstrap, 291–352. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-10.

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Uzayr, Sufyan bin. "Creating Admin Panels." In Bootstrap, 113–56. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-4.

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Uzayr, Sufyan bin. "Integrations and API." In Bootstrap, 189–202. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-7.

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Uzayr, Sufyan bin. "Crash Course in Bootstrap." In Bootstrap, 1–18. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-1.

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Uzayr, Sufyan bin. "Code Optimization." In Bootstrap, 183–88. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-6.

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Uzayr, Sufyan bin. "Bootstrap and JavaScript." In Bootstrap, 157–82. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-5.

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Uzayr, Sufyan bin. "Cheat Sheet 1." In Bootstrap, 203–44. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-8.

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Uzayr, Sufyan bin. "Cheat Sheet 4." In Bootstrap, 353–72. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003309383-11.

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Conference papers on the topic "Bootstrap"

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Motepalli, Shashank, and Hans-Arno Jacobsen. "Delay Towers to Bootstrap Blockchains." In 2024 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 397–99. IEEE, 2024. http://dx.doi.org/10.1109/icbc59979.2024.10634426.

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Engelmann, Franz, Hans Stienen, and Ernst Lebsanft. "BOOTSTRAP." In the 19th international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/253228.253457.

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Efimov, Vadim, Kirill Efimov, and Vera Kovaleva. "Anchored Bootstrap." In 2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB). IEEE, 2020. http://dx.doi.org/10.1109/csgb51356.2020.9214598.

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Schanzer, Emmanuel, Kathi Fisler, and Shriram Krishnamurthi. "Assessing Bootstrap." In SIGCSE '18: The 49th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3159450.3159498.

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"Bootstrap control." In Proceedings of the 1999 American Control Conference. IEEE, 1999. http://dx.doi.org/10.1109/acc.1999.786441.

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Bouzakaria, Nassima, Cyril Concolato, and Jean Le Feuvre. "Fast DASH bootstrap." In 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2015. http://dx.doi.org/10.1109/mmsp.2015.7340868.

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Engelbart, Douglas C. "Bootstrap alliance SIG." In CHI98: ACM Conference on Human Factors and Computing Systems. New York, NY, USA: ACM, 1998. http://dx.doi.org/10.1145/286498.286846.

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Blandford, Roger, and Stefan Funk. "The Magnetic Bootstrap." In THE FIRST GLAST SYMPOSIUM. AIP, 2007. http://dx.doi.org/10.1063/1.2757268.

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Zeng, Kai, Shi Gao, Barzan Mozafari, and Carlo Zaniolo. "The analytical bootstrap." In SIGMOD/PODS'14: International Conference on Management of Data. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2588555.2588579.

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Kim, Y. B., T. R. Willemain, J. Haddock, and G. C. Runger. "The threshold bootstrap." In the 25th conference. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/256563.256697.

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Reports on the topic "Bootstrap"

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Croft, W. J., and J. Gilmore. Bootstrap Protocol. RFC Editor, September 1985. http://dx.doi.org/10.17487/rfc0951.

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Imbens, Guido, and Konrad Menzel. A Causal Bootstrap. Cambridge, MA: National Bureau of Economic Research, July 2018. http://dx.doi.org/10.3386/w24833.

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Smith, Richard J., and Paulo Parente. Kernel block bootstrap. The IFS, July 2018. http://dx.doi.org/10.1920/wp.cem.2018.4818.

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Loh, Wei-Yin. Bootstrap and Partitioning Methods. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada290662.

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Horowitz, Joel L. Bootstrap methods in econometrics. The IFS, September 2018. http://dx.doi.org/10.1920/wp.cem.2018.5318.

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Wu, Yanlin, and R. B. White. Numerical simulation of Bootstrap Current. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/10160602.

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Kessel, C. E. Bootstrap current in a tokamak. Office of Scientific and Technical Information (OSTI), March 1994. http://dx.doi.org/10.2172/142540.

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Wu, Yanlin, and R. B. White. Numerical simulation of Bootstrap Current. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/6484029.

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Hjort, Nils L. Bayesian Nonparametric Bootstrap Confidence Intervals. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada161786.

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Andrews, Isaiah, and Jesse Shapiro. Bootstrap Diagnostics for Irregular Estimators. Cambridge, MA: National Bureau of Economic Research, January 2024. http://dx.doi.org/10.3386/w32038.

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