Relevant bibliographies by topics / Bootstrap sample size

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bootstrap sample size'

Author: Grafiati
Published: 11 December 2022
Last updated: 26 January 2023

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Journal articles on the topic "Bootstrap sample size"

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Shao, Jun. "Bootstrap sample size in nonregular cases." Proceedings of the American Mathematical Society 122, no. 4 (1994): 1251. http://dx.doi.org/10.1090/s0002-9939-1994-1227529-8.

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Thiele, Christian, and Gerrit Hirschfeld. "Confidence intervals and sample size planning for optimal cutpoints." PLOS ONE 18, no. 1 (2023): e0279693. http://dx.doi.org/10.1371/journal.pone.0279693.

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Various methods are available to determine optimal cutpoints for diagnostic measures. Unfortunately, many authors fail to report the precision at which these optimal cutpoints are being estimated and use sample sizes that are not suitable to achieve an adequate precision. The aim of the present study is to evaluate methods to estimate the variance of cutpoint estimations based on published descriptive statistics (‘post-hoc’) and to discuss sample size planning for estimating cutpoints. We performed a simulation study using widely-used methods to optimize the Youden index (empirical, normal, and transformed normal method) and three methods to determine confidence intervals (the delta method, the parametric bootstrap, and the nonparametric bootstrap). We found that both the delta method and the parametric bootstrap are suitable for post-hoc calculation of confidence intervals, depending on the sample size, the distribution of marker values, and the correctness of model assumptions. On average, the parametric bootstrap in combination with normal-theory-based cutpoint estimation has the best coverage. The delta method performs very well for normally distributed data, except in small samples, and is computationally more efficient. Obviously, not every combination of distributions, cutpoint optimization methods, and optimized metrics can be simulated and a lot of the literature is concerned specifically with cutpoints and confidence intervals for the Youden index. This complicates sample size planning for studies that estimate optimal cutpoints. As a practical tool, we introduce a web-application that allows for running simulations of width and coverage of confidence intervals using the percentile bootstrap with various distributions and cutpoint optimization methods.
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Geluk, Jaap, and Haan de. "On bootstrap sample size in extreme value theory." Publications de l'Institut Mathematique 71, no. 85 (2002): 21–26. http://dx.doi.org/10.2298/pim0271021g.

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Li, Jialiang, Bee Choo Tai, and David J. Nott. "Confidence interval for the bootstrapP-value and sample size calculation of the bootstrap test." Journal of Nonparametric Statistics 21, no. 5 (2009): 649–61. http://dx.doi.org/10.1080/10485250902770035.

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Stamps, Arthur E. "Bootstrap Investigation of Respondent Sample Size for Environmental Preference." Perceptual and Motor Skills 75, no. 1 (1992): 220–22. http://dx.doi.org/10.2466/pms.1992.75.1.220.

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Although environmental researchers use many different sizes of respondent samples, few environmental studies provide statistical rationales for selecting those sizes. This article utilizes bootstrap analysis to calculate split-block correlations of preferences of randomly selected respondents to a variety of environmental stimuli. Two analytic methods were used, raw score ratings and comparative choice. Analysis showed split-block correlations of .90 and higher could be achieved with 25 to 30 respondents with rating protocols and 10 to 15 respondents with comparative-choice protocols.
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Wei, Bei, Stephen M. S. Lee, and Xiyuan Wu. "Stochastically optimal bootstrap sample size for shrinkage-type statistics." Statistics and Computing 26, no. 1-2 (2014): 249–62. http://dx.doi.org/10.1007/s11222-014-9493-x.

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Chung, Kam‐Hin, and Stephen M. S. Lee. "Optimal Bootstrap Sample Size in Construction of Percentile Confidence Bounds." Scandinavian Journal of Statistics 28, no. 1 (2001): 225–39. http://dx.doi.org/10.1111/1467-9469.00233.

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Acquah, Henry de-Graft. "A Comparison of Bootstrap and Monte Carlo Approaches to Testing for Symmetry in the Granger and Lee Error Correction Model." Information Management and Business Review 5, no. 5 (2013): 240–44. http://dx.doi.org/10.22610/imbr.v5i5.1048.

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In this paper, I investigate the power of the Granger and Lee model of asymmetry via bootstrap and Monte Carlo techniques. The simulation results indicate that sample size, level of asymmetry and the amount of noise in the data generating process are important determinants of the power of the test for asymmetry based on bootstrap and Monte Carlo techniques. Additionally, the simulation results suggest that both bootstrap and Monte Carlo methods are successful in rejecting the false null hypothesis of symmetric adjustment in large samples with small error size and strong levels of asymmetry. In large samples, with small error size and strong levels of asymmetry, the results suggest that asymmetry test based on Monte Carlo methods achieve greater power gains when compared with the test for asymmetry based on bootstrap. However, in small samples, with large error size and subtle levels of asymmetry, the results suggest that asymmetry test based on bootstrap is more powerful than those based on the Monte Carlo methods. I conclude that both bootstrap and Monte Carlo algorithms provide valuable tools for investigating the power of the test of asymmetry.
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Cao, Leilei, Lulu Cao, Lei Guo, Kui Liu, and Xin Ding. "Reliability estimation for drive axle of wheel loader under extreme small sample." Advances in Mechanical Engineering 11, no. 3 (2019): 168781401983684. http://dx.doi.org/10.1177/1687814019836849.

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It is difficult to have enough samples to implement the full-scale life test on the loader drive axle due to high cost. But the extreme small sample size can hardly meet the statistical requirements of the traditional reliability analysis methods. In this work, the method of combining virtual sample expanding with Bootstrap is proposed to evaluate the fatigue reliability of the loader drive axle with extreme small sample. First, the sample size is expanded by virtual augmentation method to meet the requirement of Bootstrap method. Then, a modified Bootstrap method is used to evaluate the fatigue reliability of the expanded sample. Finally, the feasibility and reliability of the method are verified by comparing the results with the semi-empirical estimation method. Moreover, from the practical perspective, the promising result from this study indicates that the proposed method is more efficient than the semi-empirical method. The proposed method provides a new way for the reliability evaluation of costly and complex structures.
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Shaukat, S. Shahid, Toqeer Ahmed Rao, and Moazzam A. Khan. "Impact of sample size on principal component analysis ordination of an environmental data set: effects on eigenstructure." Ekológia (Bratislava) 35, no. 2 (2016): 173–90. http://dx.doi.org/10.1515/eko-2016-0014.

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AbstractIn this study, we used bootstrap simulation of a real data set to investigate the impact of sample size (N = 20, 30, 40 and 50) on the eigenvalues and eigenvectors resulting from principal component analysis (PCA). For each sample size, 100 bootstrap samples were drawn from environmental data matrix pertaining to water quality variables (p = 22) of a small data set comprising of 55 samples (stations from where water samples were collected). Because in ecology and environmental sciences the data sets are invariably small owing to high cost of collection and analysis of samples, we restricted our study to relatively small sample sizes. We focused attention on comparison of first 6 eigenvectors and first 10 eigenvalues. Data sets were compared using agglomerative cluster analysis using Ward’s method that does not require any stringent distributional assumptions.
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Dissertations / Theses on the topic "Bootstrap sample size"

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Song, Juhee. "Bootstrapping in a high dimensional but very low sample size problem." Texas A&M University, 2003. http://hdl.handle.net/1969.1/3853.

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High Dimension, Low Sample Size (HDLSS) problems have received much attention recently in many areas of science. Analysis of microarray experiments is one such area. Numerous studies are on-going to investigate the behavior of genes by measuring the abundance of mRNA (messenger RiboNucleic Acid), gene expression. HDLSS data investigated in this dissertation consist of a large number of data sets each of which has only a few observations. We assume a statistical model in which measurements from the same subject have the same expected value and variance. All subjects have the same distribution up to location and scale. Information from all subjects is shared in estimating this common distribution. Our interest is in testing the hypothesis that the mean of measurements from a given subject is 0. Commonly used tests of this hypothesis, the t-test, sign test and traditional bootstrapping, do not necessarily provide reliable results since there are only a few observations for each data set. We motivate a mixture model having C clusters and 3C parameters to overcome the small sample size problem. Standardized data are pooled after assigning each data set to one of the mixture components. To get reasonable initial parameter estimates when density estimation methods are applied, we apply clustering methods including agglomerative and K-means. Bayes Information Criterion (BIC) and a new criterion, WMCV (Weighted Mean of within Cluster Variance estimates), are used to choose an optimal number of clusters. Density estimation methods including a maximum likelihood unimodal density estimator and kernel density estimation are used to estimate the unknown density. Once the density is estimated, a bootstrapping algorithm that selects samples from the estimated density is used to approximate the distribution of test statistics. The t-statistic and an empirical likelihood ratio statistic are used, since their distributions are completely determined by the distribution common to all subject. A method to control the false discovery rate is used to perform simultaneous tests on all small data sets. Simulated data sets and a set of cDNA (complimentary DeoxyriboNucleic Acid) microarray experiment data are analyzed by the proposed methods.
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Walters, Stephen John. "The use of bootstrap methods for estimating sample size and analysing health-related quality of life outcomes (particularly the SF-36)." Thesis, University of Sheffield, 2003. http://etheses.whiterose.ac.uk/6053/.

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Health-Related Quality of Life (HRQoL) measures are becoming increasingly used in clinical trials and health services research, both as primary and secondary outcome measures. Investigators are now asking statisticians for advice on how to plan (e. g. sample size) and analyse studies using HRQoI_ outcomes. HRQoL outcomes like the SF-36 are usually measured on an ordinal scale. However, most investigators assume that there exists an underlying continuous latent variable that measures HRQoL, and that the actual measured outcomes (the ordered categories), reflect contiguous intervals along this continuum. The ordinal scaling of HRQoL measures means they tend to generate data that have discrete, bounded and skewed distributions. Thus, standard methods of analysis such as the t-test and linear regression that assume Normality and constant variance may not be appropriate. For this reason, non-parametric methods are often used to analyse HRQoL data. The bootstrap is one such computer intensive non-parametric method for estimating sample sizes and analysing data. From a review of the literature, I found five methods of estimating sample sizes for two-group cross-sectional comparisons of HRQoL outcomes. All five methods (amongst other factors) require the specification of an effect size, which varies according to the method of sample size estimation. The empirical effect sizes calculated from the various datasets suggested that large differences in HRQoL (as measured by the SF-36) between groups are unlikely, particularly from the RCT comparisons. Most of the observed effect sizes were mainly in the 'small' to 'moderate' range (0.2 to 0.5) using Cohen's (1988) criteria. I compared the power of various methods of sample size estimation for two-group cross-sectional study designs via bootstrap simulation. The results showed that under the location shift alternative hypothesis, conventional methods of sample size estimation performed well, particularly Whitehead's (1993) method. Whitehead's method is recommended if the HRQoL outcome has a limited number of discrete values (< 7) and/or the expected proportion of cases at either of the bounds is high. If a pilot dataset is readily available (to estimate the shape of the distribution) then bootstrap simulation may provide a more accurate and reliable estimate, than conventional methods. Finally, I used the bootstrap for hypothesis testing and the estimation of standard errors and confidence intervals for parameters, in four datasets (which illustrate the different aspects of study design). I then compared and contrasted the bootstrap with standard methods of analysing HRQoL outcomes as described in Fayers and Machin (2000). Overall, in the datasets studied with the SF-36 outcome the use of the bootstrap for estimating sample sizes and analysing HRQoL data appears to produce results similar to conventional statistical methods. Therefore, the results of this thesis suggest that bootstrap methods are not more appropriate for analysing HRQoL outcome data than standard methods. This result requires confirmation with other HRQoL outcome measures, interventions and populations.
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Rao, Youlan. "Statistical Analysis of Microarray Experiments in Pharmacogenomics." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1244756072.

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Good, Norman Markus. "Methods for estimating the component biomass of a single tree and a stand of trees using variable probability sampling techniques." Thesis, Queensland University of Technology, 2001. https://eprints.qut.edu.au/37097/1/37097_Good_2001.pdf.

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This thesis developed multistage sampling methods for estimating the aggregate biomass of selected tree components, such as leaves, branches, trunk and total, in woodlands in central and western Queensland. To estimate the component biomass of a single tree randomised branch sampling (RBS) and importance sampling (IS) were trialed. RBS and IS were found to reduce the amount of time and effort to sample tree components in comparison with other standard destructive sampling methods such as ratio sampling, especially when sampling small components such as leaves and small twigs. However, RBS did not estimate leaf and small twig biomass to an acceptable degree of precision using current methods for creating path selection probabilities. In addition to providing an unbiased estimate of tree component biomass, individual estimates were used for developing allometric regression equations. Equations based on large components such as total biomass produced narrower confidence intervals than equations developed using ratio sampling. However, RBS does not estimate small component biomass such as leaves and small wood components with an acceptable degree of precision, and should be mainly used in conjunction with IS for estimating larger component biomass. A whole tree was completely enumerated to set up a sampling space with which RBS could be evaluated under a number of scenarios. To achieve a desired precision, RBS sample size and branch diameter exponents were varied, and the RBS method was simulated using both analytical and re-sampling methods. It was found that there is a significant amount of natural variation present when relating the biomass of small components to branch diameter, for example. This finding validates earlier decisions to question the efficacy of RBS for estimating small component biomass in eucalypt species. In addition, significant improvements can be made to increase the precision of RBS by increasing the number of samples taken, but more importantly by varying the exponent used for constructing selection probabilities. To further evaluate RBS on trees with differing growth forms from that enumerated, virtual trees were generated. These virtual trees were created using L-systems algebra. Decision rules for creating trees were based on easily measurable characteristics that influence a tree's growth and form. These characteristics included; child-to-child and children-to-parent branch diameter relationships, branch length and branch taper. They were modelled using probability distributions of best fit. By varying the size of a tree and/or the variation in the model describing tree characteristics; it was possible to simulate the natural variation between trees of similar size and fonn. By creating visualisations of these trees, it is possible to determine using visual means whether RBS could be effectively applied to particular trees or tree species. Simulation also aided in identifying which characteristics most influenced the precision of RBS, namely, branch length and branch taper. After evaluation of RBS/IS for estimating the component biomass of a single tree, methods for estimating the component biomass of a stand of trees (or plot) were developed and evaluated. A sampling scheme was developed which incorporated both model-based and design-based biomass estimation methods. This scheme clearly illustrated the strong and weak points associated with both approaches for estimating plot biomass. Using ratio sampling was more efficient than using RBS/IS in the field, especially for larger tree components. Probability proportional to size sampling (PPS) -size being the trunk diameter at breast height - generated estimates of component plot biomass that were comparable to those generated using model-based approaches. The research did, however, indicate that PPS is more precise than the use of regression prediction ( allometric) equations for estimating larger components such as trunk or total biomass, and the precision increases in areas of greater biomass. Using more reliable auxiliary information for identifying suitable strata would reduce the amount of within plot variation, thereby increasing precision. PPS had the added advantage of being unbiased and unhindered by numerous assumptions applicable to the population of interest, the case with a model-based approach. The application of allometric equations in predicting the component biomass of tree species other than that for which the allometric was developed is problematic. Differences in wood density need to be taken into account as well as differences in growth form and within species variability, as outlined in virtual tree simulations. However, the development and application of allometric prediction equations in local species-specific contexts is more desirable than PPS.
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Books on the topic "Bootstrap sample size"

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Marlowe, Christopher. The effect of decreasing sample size on the precision of GSI stock composition estimates for chinook salmon (Onchorhynchus tshawytscha) using data from the Washington Coastal and Strait of Juan de Fuca troll fisheries in 1989-1990. Washington Dept. of Fish and Wildlife, 1995.

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Book chapters on the topic "Bootstrap sample size"

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Plant, Richard E. "Variance Estimation, the Effective Sample Size, and the Bootstrap." In Spatial Data Analysis in Ecology and Agriculture Using R. CRC Press, 2018. http://dx.doi.org/10.1201/9781351189910-10.

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Amalnerkar, Eshan, Tae Hee Lee, and Woochul Lim. "Bootstrap Guided Information Criterion for Reliability Analysis Using Small Sample Size Information." In Advances in Structural and Multidisciplinary Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67988-4_25.

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Sahoo, Tanmaya Kumar, and Rachita Panda. "Estimating Uncertainty in Flood Frequency Analysis Due to Limited Sample Size Using Bootstrap Method." In Lecture Notes in Civil Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7509-6_51.

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Götze, Friedrich, and Alfredas Račkauskas. "Adaptive choice of bootstrap sample sizes." In State of the art in probability and statistics. Institute of Mathematical Statistics, 2001. http://dx.doi.org/10.1214/lnms/1215090074.

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"Variance Estimation, the Effective Sample Size, and the Bootstrap." In Spatial Data Analysis in Ecology and Agriculture Using R. CRC Press, 2012. http://dx.doi.org/10.1201/b11769-11.

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Anderson, Raymond A. "Sample Selection." In Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0020.

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Samples are small amounts chosen to represent a larger pool; sampling dips into the pool to choose which will be analysed, with conclusions extended to the larger pool. Trade-offs happen between i) having samples of sufficiently large and representative to enable reasonable results; ii) the costs associated with data collection and processing. (1) Overview—i) terminology—proper drawing and representation {random, stratified, bias, weight}, counts per subgroup {over-, under- and balanced sample}; repetitive {replacement, bootstrap, k-fold, jack-knife}; artificial increases {bagging, boosting, stacking}; ii) optimal and minimum sample sizes; iii) law of diminishing data returns. (2) THOR samples—i) sample types—training, hold-out, out-of-time, recent; ii) sample count guidelines; iii) review of observation windows and assignment; iv) sampling plan and outcome. (3) Afterthoughts—i) further review of un- and under-populated characteristics; ii) means of randomly extracting an exact number of cases; iii) housekeeping rules regarding code assignments for each sample.
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Borsci, Simone, Stefano Federici, Maria Laura Mele, Domenico Polimeno, and Alessandro Londei. "The Bootstrap Discovery Behaviour Model." In Cognitively Informed Intelligent Interfaces. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1628-8.ch015.

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The chapter focuses on the Bootstrap statistical technique for assigning measures of accuracy to sample estimates, here adopted for the first time to obtain an effective and efficient interaction evaluation. After introducing and discussing the classic debate on p value (i.e., the discovery detection rate) about estimation problems, the authors present the most used model for the estimation of the number of participants needed for an evaluation test, namely the Return On Investment model (ROI). Since the ROI model endorses a monodimensional and economical perspective in which an evaluation process, composed of only an expert technique, is sufficient to identify all the interaction problems—without distinguishing real problems (i.e., identified both experts and users) and false problems (i.e., identified only by experts)—they propose the new Bootstrap Discovery Behaviour (BDB) estimation model. Findings highlight the BDB as a functional technique favouring practitioners to optimize the number of participants needed for an interaction evaluation. Finally, three experiments show the application of the BDB model to create experimental sample sizes to test user experience of people with and without disabilities.
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"strate IBE the upper bound of a 90% confidence interval for the above aggregate metric must fall below 2.49. The required upper bound can be calculated in at least three different ways: (1) method-of-moments estimation with a Cornish-Fisher approx-imation (Hyslop et al., 2000; FDA Guidance, 2001), (2) bootstrapping (FDA Guidance, 1997), and (3) by asymptotic approximations to the mean and variance of ν and ν (Patterson, 2003; Patterson and Jones, 2002b,c). Method (1) derives from theory that assumes the inde-pendence of chi-squared variables and is more appropriate to the analysis of a parallel group design. Hence it does not fully account for the within-subject correlation that is present in data obtained from cross-over tri-als. Moreover, the approach is potentially sensitive to bias introduced by missing data and imbalance in the study data (Patterson and Jones, 2002c). Method (2), which uses the nonparametric percentile bootstrap method (Efron and Tibshirani, 1993), was the earliest suggested method of calculating the upper bound (FDA Guidance, 1997), but it has sev-eral disadvantages. Among these are that it is computationally intensive and it introduces randomness into the final calculated upper bound. Re-cent modifications to ensure consistency of the bootstrap (Shao et al., 2000) do not appear to protect the Type I error rate (Patterson and Jones, 2002c) around the mixed-scaling cut-off (0.04) unless calibration (Efron and Tibshirani, 1993) is used. Use of such a calibration technique is questionable if one is making a regulatory submission. Hence, we pre-fer to use method (3) and will illustrate its use shortly. We note that this method appears to protect against inflation of the Type I error rate in IBE and PBE testing, and the use of REML ensures unbiased esti-mates (Patterson and Jones, 2002c) in data sets with missing data and imbalance, a common occurrence in cross-over designs, (Patterson and Jones, 2002a,b). In general (Patterson and Jones, 2002a), cross-over tri-als that have been used to test for IBE and PBE have used sample sizes in excess of 20 to 30 subjects, so asymptotic testing is not unreasonable, and there is a precedent for the use of such procedures in the study of pharmacokinetics (Machado et al., 1999). We present findings here based on asymptotic normal theory using REML and not taking into account shrinkage (Patterson and Jones, 2002b,c). It is possible to account for this factor using the approach of Harville and Jeske (1992); see also Ken-ward and Roger (1997). However, this approach is not considered here in the interests of space and as the approach described below appears to control the Type I error rate for sample sizes as low as 16 (Patterson and Jones, 2002c). In a 2 × 2 cross-over trial it is not possible to estimate separately the within-and between-subject variances and hence a replicate design, where subjects receiving each formulation more than once is required." In Design and Analysis of Cross-Over Trials. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9781420036091-19.

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Conference papers on the topic "Bootstrap sample size"

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Phan, John H., Richard A. Moffitt, Andrea B. Barrett, and May D. Wang. "Improving Microarray Sample Size Using Bootstrap Data Combination." In 2008 International Multi-symposiums on Computer and Computational Sciences (IMSCCS). IEEE, 2008. http://dx.doi.org/10.1109/imsccs.2008.36.

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Ocheredko, Oleksandr. "MCMC Bootstrap Based Approach to Power and Sample Size Evaluation." In Annual Meeting of the International Society for Data Science and Analytics. ISDSA Press, 2020. http://dx.doi.org/10.35566/isdsa2019c5.

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Zhao, Yuying, and Rakkrit Duangsoithong. "Empirical Analysis using Feature Selection and Bootstrap Data for Small Sample Size Problems." In 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2019. http://dx.doi.org/10.1109/ecti-con47248.2019.8955366.

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Lakshminarayana, Ramaprasad E., Nishant Bhardwaj, and Shun Takai. "Simulation-Based and Experimental Studies of Subjective Clustering and Bootstrap Application to Subjective Clustering." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35080.

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The success of any product in today’s competitive market is dictated by its ability to satisfy the needs of the customers. In this effort, it is important to group similar needs to recognize representative needs, and then identify product requirements that can fulfill these representative needs. One approach to this is to apply Subjective Clustering (SC) to sample data (grouping of customer needs by a sample of customers); however, clusters obtained by SC give only a point estimate of the primary clusters of customer needs by the entire population of customers (population primary clusters). Applying Bootstrap to SC (BS-SC) helps engineers to make inferences on the population primary clusters. In this paper, we randomly pulled out samples of different sizes from both the simulation approach using simulation-generated population data and the empirical approach using experimental population data, and compared the accuracies of SC and BS-SC. Regardless of population sizes, when the sample size was small, BS-SC was more accurate than SC in estimating the population primary clusters. Also, the BS-SC and SC estimates were similar for both simulation and empirical approaches.
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Bhardwaj, Nishant, and Shun Takai. "Investigating the Accuracy of Subjective Clustering and Bootstrap Application to Subjective Clustering Using an Empirical Population." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14516.

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For a new product to be successful in today's market, engineers need to identify representative customer needs. One approach to identify representative needs from a large number of needs is Subjective Clustering (SC). A set of clusters obtained from SC is a point estimate of clusters generated by a population of customers. Another approach is to apply Bootstrap (BS) to SC. By applying BS to SC, engineers can draw an inference about population primary clusters. This paper compares the accuracy of estimating population primary clusters using SC and Bootstrap applied to SC (BS-SC). The authors recruited participants to perform the clustering experiments and assumed that these participants consist a population. The authors randomly sampled subsets of participants and evaluated how accurately SC and BS-SC identify population primary clusters. When the sample size is small relative to the population, BS-SC estimated population primary clusters more accurately than SC.
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Brooker, Daniel C., and Geoffrey K. Cole. "Accurate Calculation of Confidence Intervals on Predicted Extreme Met-Ocean Conditions When Using Small Datasets." In ASME 2004 23rd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/omae2004-51160.

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Estimation of high return period values of wind and wave conditions is usually done using a limited sample of data from measurement or hindcast studies. Because a finite sample size is used, the reliability of estimates is usually evaluated by constructing confidence intervals around design values such as the 100 year return value. In this paper, a numerical simulation study is used to compare the accuracy of calculated confidence intervals using several different calculation methods: the asymptotic normal, parametric bootstrap and profile likelihood methods. The accuracy of each method for different sample sizes is assessed for the truncated Weibull distribution. Based on these results, a profile likelihood method for estimation of confidence intervals is suggested for use when dealing with small datasets.
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Randell, David, Elena Zanini, Michael Vogel, Kevin Ewans, and Philip Jonathan. "Omnidirectional Return Values for Storm Severity From Directional Extreme Value Models: The Effect of Physical Environment and Sample Size." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23156.

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Ewans and Jonathan [2008] shows that characteristics of extreme storm severity in the northern North Sea vary with storm direction. Jonathan et al. [2008] demonstrates, when directional effects are present, that omnidirectional return values should be estimated using a directional extreme value model. Omnidirectional return values so calculated are different in general to those estimated using a model which incorrectly assumes stationarity with respect to direction. The extent of directional variability of extreme storm severity depends on a number of physical factors, including fetch variability. Our ability to assess directional variability of extreme value parameters and return values also improves with increasing sample size in general. In this work, we estimate directional extreme value models for samples of hindcast storm peak significant wave height from locations in ocean basins worldwide, for a range of physical environments, sample sizes and periods of observation. At each location, we compare distributions of omnidirectional 100-year return values estimated using a directional model, to those (incorrectly) estimated assuming stationarity. The directional model for peaks over threshold of storm peak significant wave height is estimated using a non-homogeneous point process model as outlined in Randell et al. [2013]. Directional models for extreme value threshold (using quantile regression), rate of occurrence of threshold exceedances (using a Poisson model), and size of exceedances (using a generalised Pareto model) are estimated. Model parameters are described as smooth functions of direction using periodic B-splines. Parameter estimation is performed using maximum likelihood estimation penalised for parameter roughness. A bootstrap re-sampling procedure, encompassing all inference steps, quantifies uncertainties in, and dependence structure of, parameter estimates and omnidirectional return values.
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Adnan, Md Nasim. "Improving the random forest algorithm by randomly varying the size of the bootstrap samples." In 2014 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 2014. http://dx.doi.org/10.1109/iri.2014.7051904.

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Picheny, Victor, Nam-Ho Kim, and Raphael T. Haftka. "Conservative Estimations of Reliability With Limited Sampling." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35465.

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Abstract:
The objective of this paper is to provide a method of safely estimating reliability based on small samples. First, it is shown that the commonly used estimators of the parameters of the normal distribution function are biased, and they tend to lead to unconservative estimates of reliability. Then, two ways of making this estimation conservative are proposed: (1) adding constraints when a distribution is fitted to the data to bias it to be conservative, and (2) using the bootstrap method to estimate the bias needed for a given level of conservativeness. The relationship between the accuracy and the conservativeness of the estimates is explored for a normal distribution. In particular, detailed results are presented for the case when the goal is 95% likelihood to be conservative. The bootstrap approach is found to be more accurate for this level of conservativeness. It is then applied to the reliability analysis of a composite panel under thermal loading. Finally, we explore the influence of sample sizes and target probability of failure on estimates quality, and show that for a constant level of conservativeness, small samples and low probabilities can lead to a high risk of large overestimation while this risk is limited to a very reasonable value for samples above.
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10

Song, Jiazhang, Li-Yang Chen, Mau-Chung Frank Chang, Sudhakar Pamarti, and Chih-Kong Ken Yang. "A 14-bit 1-GS/s SiGe Bootstrap Sampler for High Resolution ADC with 250-MHz Input." In 2022 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2022. http://dx.doi.org/10.1109/iscas48785.2022.9937559.

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