Artículos de revistas: "Bootstrap"

1

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

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2

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 (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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3

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 (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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4

Breivik, Øyvind, and Ole Johan Aarnes. "Efficient bootstrap estimates for tail statistics." Natural Hazards and Earth System Sciences 17, no. 3 (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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5

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 (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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6

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

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7

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

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8

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

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9

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

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10

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

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11

Steinmetz, Julia, and Carsten Jentsch. "Bootstrap consistency for the Mack bootstrap." Insurance: Mathematics and Economics 115 (March 2024): 83–121. http://dx.doi.org/10.1016/j.insmatheco.2024.01.001.

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12

Wanishsakpong, Wandee, Kantima Sodrung, and Ampai Thongteeraparp. "A Comparison of Nonparametric Statistics and Bootstrap Methods for Testing Two Independent Populations with Unequal Variance." International Journal of Analysis and Applications 21 (April 17, 2023): 36. http://dx.doi.org/10.28924/2291-8639-21-2023-36.

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The common parametric statistics used for testing two independent populations have often required the assumptions of normality and equal variances. Nonparametric tests have been used when assumptions of parametric tests cannot be achieved. However, some studies found nonparametric tests to be too conservative and less powerful than parametric tests. Bootstrap methods are also alternative tests when assumptions of parametric tests are violated, but they have small size limitations. Later, nonparametric tests when pooled with the bootstrap methods may overcome the powerful test and small sample sizes issue. Thus, the purpose of this study was to apply the bootstrap method together with nonparametric statistics and compare the efficiency of nonparametric tests and bootstraps methods when pooled with nonparametric tests for testing the mean difference between two independent populations with unequal variance. The Yuen Welch Test (YW), Brunner-Munzel Test (BM), Bootstrap Yuen Welch Test (BYW) and Bootstrap Brunner-Munzel Test (BBM) were studied via Monte Carlo simulation with non-normal population distributions. The results show that the probability of a type I error of all four test statistics could be controlled for all situations. The Brunner-Munzel test (BM) had the highest power and the best efficiency in the case of mean difference ratio increases. The Bootstrap Yuen Welch Test (BYW) had the highest power when the sample size was small.

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13

Mohd Noh, Muhamad Husnain, Mohd Akramin Mohd Romlay, Chuan Zun Liang, Mohd Shamil Shaari, and Akiyuki Takahashi. "Analysis of stress intensity factor for fatigue crack using bootstrap S-version finite element model." International Journal of Structural Integrity 11, no. 4 (2020): 579–89. http://dx.doi.org/10.1108/ijsi-10-2019-0108.

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PurposeFailure of the materials occurs once the stress intensity factor (SIF) overtakes the material fracture toughness. At this level, the crack will grow rapidly resulting in unstable crack growth until a complete fracture happens. The SIF calculation of the materials can be conducted by experimental, theoretical and numerical techniques. Prediction of SIF is crucial to ensure safety life from the material failure. The aim of the simulation study is to evaluate the accuracy of SIF prediction using finite element analysis.Design/methodology/approachThe bootstrap resampling method is employed in S-version finite element model (S-FEM) to generate the random variables in this simulation analysis. The SIF analysis studies are promoted by bootstrap S-version Finite Element Model (BootstrapS-FEM). Virtual crack closure-integral method (VCCM) is an important concept to compute the energy release rate and SIF. The semielliptical crack shape is applied with different crack shape aspect ratio in this simulation analysis. The BootstrapS-FEM produces the prediction of SIFs for tension model.FindingsThe mean of BootstrapS-FEM is calculated from 100 samples by the resampling method. The bounds are computed based on the lower and upper bounds of the hundred samples of BootstrapS-FEM. The prediction of SIFs is validated with Newman–Raju solution and deterministic S-FEM within 95 percent confidence bounds. All possible values of SIF estimation by BootstrapS-FEM are plotted in a graph. The mean of the BootstrapS-FEM is referred to as point estimation. The Newman–Raju solution and deterministic S-FEM values are within the 95 percent confidence bounds. Thus, the BootstrapS-FEM is considered valid for the prediction with less than 6 percent of percentage error.Originality/valueThe bootstrap resampling method is employed in S-FEM to generate the random variables in this simulation analysis.

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14

Flynn, Terry N., and Tim J. Peters. "Cluster randomized trials: Another problem for cost-effectiveness ratios." International Journal of Technology Assessment in Health Care 21, no. 3 (2005): 403–9. http://dx.doi.org/10.1017/s0266462305050531.

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Objectives:This work has investigated under what conditions cost-effectiveness data from a cluster randomized trial (CRT) are suitable for analysis using a cluster-adjusted nonparametric bootstrap. The bootstrap's main advantages are in dealing with skewed data and its ability to take correlations between costs and effects into account. However, there are known theoretical problems with a commonly used cluster bootstrap procedure, and the practical implications of these require investigation.Methods:Simulations were used to estimate the coverage of confidence intervals around incremental cost-effectiveness ratios from CRTs using two bootstrap methods.Results:The bootstrap gave excessively narrow confidence intervals, but there was evidence to suggest that, when the number of clusters per treatment arm exceeded 24, it might give acceptable results. The method that resampled individuals as well as clusters did not perform well when cost and effectiveness data were correlated.Conclusions:If economic data from such trials are to be analyzed adequately, then there is a need for further investigations of more complex bootstrap procedures. Similarly, further research is required on methods such as the net benefit approach.

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15

Hall, P., and Y. Maesono. "A weighted bootstrap approach to bootstrap iteration." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 62, no. 1 (2000): 137–44. http://dx.doi.org/10.1111/1467-9868.00224.

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16

Silva, Alisson de Oliveira, Jonas Weverson de Ararújo Silva, and Patrícia L. Espinheira. "Bootstrap-based inferential improvements to the simplex nonlinear regression model." PLOS ONE 17, no. 8 (2022): e0272512. http://dx.doi.org/10.1371/journal.pone.0272512.

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In this paper we evaluate the performance of point and interval estimators based on the maximum likelihood(ML) method for the nonlinear simplex regression model. Inferences based on traditional maximum likelihood estimation have good asymptotic properties, but their performance in small samples may not be satisfactory. At out set we consider the maximum likelihood estimation for the parameters of the nonlinear simplex regression model, and so we introduced a bootstrap-based correction for such estimators of this model. We also develop the percentile and bootstrapt confidence intervals for those parameters as competitors to the traditional approximate confidence interval based on the asymptotic normality of the maximum likelihood estimators (MLEs). We then numerically evaluate the performance of these different methods for estimating the simplex regression model. The numerical evidence favors inference based on the bootstrap method, in special the bootstrapt interval, which was decisive in an application to real data.

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17

Oram, Hugh, David McWilliams, Eddie Hobbs, et al. "Bootstrap Elevation." Books Ireland, no. 288 (2006): 230. http://dx.doi.org/10.2307/20632965.

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18

Boos, Dennis, and Leonard Stefanski. "Efron's bootstrap." Significance 7, no. 4 (2010): 186–88. http://dx.doi.org/10.1111/j.1740-9713.2010.00463.x.

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19

Aronsson, M., L. Arvastson, J. Holst, B. Lindoff, and A. Svensson. "Bootstrap Control." IEEE Transactions on Automatic Control 51, no. 1 (2006): 28–37. http://dx.doi.org/10.1109/tac.2005.861722.

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20

Brandt, A., J. Brannick, K. Kahl, and I. Livshits. "Bootstrap AMG." SIAM Journal on Scientific Computing 33, no. 2 (2011): 612–32. http://dx.doi.org/10.1137/090752973.

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21

Kiselev, V. V., and S. A. Timofeev. "Cosmological bootstrap." Physics of Particles and Nuclei Letters 9, no. 2 (2012): 111–28. http://dx.doi.org/10.1134/s1547477112020100.

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22

Shalizi, Cosma. "The Bootstrap." American Scientist 98, no. 3 (2010): 186. http://dx.doi.org/10.1511/2010.84.186.

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23

YOUNG, G. A., and H. E. DANIELS. "Bootstrap bias." Biometrika 77, no. 1 (1990): 179–85. http://dx.doi.org/10.1093/biomet/77.1.179.

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24

DAVISON, A. C., D. V. HINKLEY, and B. J. WORTON. "Bootstrap likelihoods." Biometrika 79, no. 1 (1992): 113–30. http://dx.doi.org/10.1093/biomet/79.1.113.

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25

Hinkley, David V. "Bootstrap Methods." Journal of the Royal Statistical Society: Series B (Methodological) 50, no. 3 (1988): 321–37. http://dx.doi.org/10.1111/j.2517-6161.1988.tb01731.x.

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26

Adler, Joan. "Bootstrap percolation." Physica A: Statistical Mechanics and its Applications 171, no. 3 (1991): 453–70. http://dx.doi.org/10.1016/0378-4371(91)90295-n.

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27

Galichon, Alfred, and Marc Henry. "Dilation bootstrap." Journal of Econometrics 177, no. 1 (2013): 109–15. http://dx.doi.org/10.1016/j.jeconom.2013.07.001.

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28

Shi, Sheng G. "Local bootstrap." Annals of the Institute of Statistical Mathematics 43, no. 4 (1991): 667–76. http://dx.doi.org/10.1007/bf00121646.

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29

Hansson, Sven Ove. "Bootstrap Contraction." Studia Logica 101, no. 5 (2012): 1013–29. http://dx.doi.org/10.1007/s11225-012-9418-7.

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30

Kim, Jae H. "Bootstrap-After-Bootstrap Prediction Intervals for Autoregressive Models." Journal of Business & Economic Statistics 19, no. 1 (2001): 117–28. http://dx.doi.org/10.1198/07350010152472670.

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31

Davidson, Russell. "Diagnostics for the bootstrap and fast double bootstrap." Econometric Reviews 36, no. 6-9 (2017): 1021–38. http://dx.doi.org/10.1080/07474938.2017.1307918.

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32

Peters, Gareth W., Mario V. Wüthrich, and Pavel V. Shevchenko. "Chain ladder method: Bayesian bootstrap versus classical bootstrap." Insurance: Mathematics and Economics 47, no. 1 (2010): 36–51. http://dx.doi.org/10.1016/j.insmatheco.2010.03.007.

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33

Shoemaker, O. J., and P. K. Pathak. "THE SEQUENTIAL BOOTSTRAP: A COMPARISON WITH REGULAR BOOTSTRAP." Communications in Statistics - Theory and Methods 30, no. 8-9 (2001): 1661–74. http://dx.doi.org/10.1081/sta-100105691.

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34

Hodoshima, Jiro, and Masakazu Ando. "Bootstrapping stochastic regression models under homoskedasticity: wild bootstrapvs. pairs bootstrap." Journal of Statistical Computation and Simulation 80, no. 11 (2010): 1225–35. http://dx.doi.org/10.1080/00949650903014971.

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35

Pal, Manisha. "Asymptotic Confidence Intervals for the Optimal Cost in Newsboy Problem." Calcutta Statistical Association Bulletin 46, no. 3-4 (1996): 245–52. http://dx.doi.org/10.1177/0008068319960308.

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This paper attempts to set approximate confidence limits for tlie expected cost resulting from an estimated optimal ordering policy in the newsboy problem, when the demand distribution is completely unknown. The asymptotic distribution of the estimated cost, the bootstrap and the bootstrap t procedures have been used for the purpose.

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36

Jianping, Wan, Zhang Kongsheng, and Chen Hui. "The bootstrap and Bayesian bootstrap method in assessing bioequivalence." Chaos, Solitons & Fractals 41, no. 5 (2009): 2246–49. http://dx.doi.org/10.1016/j.chaos.2008.08.035.

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37

Fuh, Cheng-Der, and Edward H. Ip. "Bootstrap and Bayesian bootstrap clones for censored Markov chains." Journal of Statistical Planning and Inference 128, no. 2 (2005): 459–74. http://dx.doi.org/10.1016/j.jspi.2003.11.009.

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38

Mammen, Enno. "Bootstrap and Wild Bootstrap for High Dimensional Linear Models." Annals of Statistics 21, no. 1 (1993): 255–85. http://dx.doi.org/10.1214/aos/1176349025.

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39

Flachaire, Emmanuel. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap." Computational Statistics & Data Analysis 49, no. 2 (2005): 361–76. http://dx.doi.org/10.1016/j.csda.2004.05.018.

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40

Kelechi, Acha, Chigozie, Kintunde Mutairu Oyewale, and Anayo Charles Iwuji. "Simulating Parametric and Nonparametric Models." Journal of Mathematics and Statistics Studies 4, no. 2 (2023): 79–91. http://dx.doi.org/10.32996/jmss.2023.4.2.9.

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The purpose of this paper was to investigate the performance of the parametric bootstrap data generating processes (DGPs) methods and to compare the parametric and nonparametric bootstrap (DGPs) methods for estimating the standard error of simple linear regression (SLR) under various assessment conditions. When the performance of the parametric bootstrap method was investigated, simple linear models were employed to fit the data. With the consideration of the different bootstrap levels and sample sizes, a total of twelve parametric bootstrap models were examined. Three hypothetical and one real datasets were used as the basis to define the population distributions and the “true” SEEs. A bootstrap paper was conducted on different parametric and nonparametric bootstrap (DGPs) methods reflecting three levels for group proficiency differences, three levels of sample sizes, three test lengths and three bootstrap levels. Bias of the SLR, standard errors of the SLR, root mean square errors of the SLR, were calculated and used to evaluate and compare the bootstrap results. The main findings from this bootstrap paper were as follows: (i) The parametric bootstrap DGP models with larger bootstrap levels generally produced smaller bias likewise a larger sample size. (ii) The parametric bootstrap models with a higher bootstrap level generally yielded more accurate estimates of the standard error than the corresponding models with lower bootstrap level. (iii) The nonparametric bootstrap method generally produced less accurate estimates of the standard error than the parametric bootstrap method. However, as the sample size increased, the differences between the two bootstrap methods became smaller. When the sample size was equal to or larger than 3,000, say 10000, the differences between the nonparametric bootstrap DGP method and the parametric bootstrap DGP model that produced the smallest RMSE were very small. (4) Of all the models considered in this paper, parametric bootstrap DGP models with higher bootstrap performed better under most bootstrap conditions. (5) Aside from method effects, sample size and test length had the most impact on estimating the Simple Linear Regression.

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41

Calhoun, Gray. "BLOCK BOOTSTRAP CONSISTENCY UNDER WEAK ASSUMPTIONS." Econometric Theory 34, no. 6 (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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42

Zhang, Ruda, and Roger Ghanem. "Normal-Bundle Bootstrap." SIAM Journal on Mathematics of Data Science 3, no. 2 (2021): 573–92. http://dx.doi.org/10.1137/20m1356002.

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43

de Angelis, Daniela, and G. Alastair Young. "Smoothing the Bootstrap." International Statistical Review / Revue Internationale de Statistique 60, no. 1 (1992): 45. http://dx.doi.org/10.2307/1403500.

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44

Davison, A. C., D. V. Hinkley, and E. Schechtman. "Efficient Bootstrap Simulation." Biometrika 73, no. 3 (1986): 555. http://dx.doi.org/10.2307/2336519.

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45

Fushiki, Tadayoshi, Fumiyasu Komaki, and Kazuyuki Aihara. "Nonparametric bootstrap prediction." Bernoulli 11, no. 2 (2005): 293–307. http://dx.doi.org/10.3150/bj/1116340296.

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46

Bowden, Roger J., and Ah Boon Sim. "The Privacy Bootstrap." Journal of Business & Economic Statistics 10, no. 3 (1992): 337. http://dx.doi.org/10.2307/1391546.

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47

Binhimd, Sulafah, and Bashair Almalki. "Bootstrap prediction intervals." Applied Mathematical Sciences 12, no. 17 (2018): 841–48. http://dx.doi.org/10.12988/ams.2018.8686.

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48

Bowden, Roger J., and Ah Boon Sim. "The Privacy Bootstrap." Journal of Business & Economic Statistics 10, no. 3 (1992): 337–45. http://dx.doi.org/10.1080/07350015.1992.10509909.

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49

Poland, David, and David Simmons-Duffin. "The conformal bootstrap." Nature Physics 12, no. 6 (2016): 535–39. http://dx.doi.org/10.1038/nphys3761.

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50

Ashrafi, M. "Chiral modular bootstrap." International Journal of Modern Physics A 34, no. 28 (2019): 1950168. http://dx.doi.org/10.1142/s0217751x19501689.

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Using modular bootstrap we show the lightest primary fields of a unitary compact two-dimensional conformal field theory (with [Formula: see text], [Formula: see text]) has a conformal weight [Formula: see text]. This implies that the upper bound on the dimension of the lightest primary fields depends on their spin. In particular if the set of lightest primary fields includes extremal or near extremal states whose spin to dimension ratio [Formula: see text], the corresponding dimension is [Formula: see text]. From AdS/CFT correspondence, we obtain an upper bound on the spectrum of black hole in three-dimensional gravity. Our results show that if the first primary fields have large spin, the corresponding three-dimensional gravity has extremal or near extremal BTZ black hole.

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Artículos de revistas sobre el tema "Bootstrap"

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