Journal articles: 'Bootstrap resampling method'

1

Putra G, Aditio, Muhammad Arif Tiro, and Muhammad Kasim Aidid. "Metode Boostrap dan Jackknife dalam Mengestimasi Parameter Regresi Linear Ganda (Kasus: Data Kemiskinan Kota Makassar Tahun 2017)." VARIANSI: Journal of Statistics and Its application on Teaching and Research 1, no. 2 (July 12, 2019): 32. http://dx.doi.org/10.35580/variansiunm12895.

Full text

Abstract:

Abstrak Metode kuadrat terkecil merupakan metode standar untuk mengestimasi nilai parameter model regresi linear. Metode tersebut dibangun berdasarkan asumsi error bersifat identik dan independen, serta berdistribusi normal. Apabila asumsi tidak terpenuhi maka metode ini tidak akurat. Alternatif untuk mengatasi hal tersebut adalah dengan menggunakan metode resampling. Adapun metode resampling yang digunakan dalam penelitian ini yaitu metode bootstrap dan Jackknife. Terlebih dahulu dilakukan estimasi nilai parameter regresi untuk analisis data kemiskinan Kota Makassar Tahun 2017. Data tersebut merupakan data sekunder diperoleh dari BAPPEDA Kota Makassar. Dari uji asumsi klasik diperoleh bahwa model tidak bersifat homoskedastis dan residual tidak berdistribusi normal sehingga model regresi yang diperoleh tidak dapat dipertanggungjawabkan. Metode bootstrap dan jackknife yang dikenalkan disini menggunakan program R untuk mencari nilai bias dan nilai standar errornya. Estimasi parameter model regresi linear berganda dari metode resampling bootstrap dengan B=200 dan B=500 serta metode resampling jackknife Terhapus-1 diperoleh model regresi. Hasil yang didapat dalam penelitian ini, metode jackknife merupakan metode yang efisien dibandingkan dengan metode bootstrap, hal ini didukung dengan kecilnya tingkat standar error dan nilai biasnya yang dihasilkan. Kata Kunci: Regrei, Resampling, Bootsrap, JaccknifeAbstract. The Ordinary least squares method is a standard method for estimating the parameter values of a linear regression model. The method is built based on error assumptions that are identical and independent, and are normally distributed. If the assumptions are not met, this method is not accurate. The alternative to overcome this is to use the resampling method. The resampling method used in this study is bootstrap and jackknife methods. First, estimation of regression parameter values for analysis of poverty data in Makassar City in 2017. The data is secondary data obtained from the BAPPEDA of Makassar City. From the classic assumption test, it is obtained that the model is not homosexedastic and residual is not normally distributed so that the regression model obtained cannot be accounted for. Bootstrap and jackknife methods are introduced here using the R program to find the value of the bias and the standard error values. Parameter estimation of multiple linear regression models from Bootstrap resampling method with B= 200, B= 500 and jackknife deleted-1 resampling method obtained regression models. The results obtained in this study, Jackknife method is an efficient method compared with the bootstrap method, and this is supported by the small standard level error and bias in resulting value.Keywords: regression, resampling, bootstrap, jackknife.

APA, Harvard, Vancouver, ISO, and other styles

2

S.W., Fransiska Grace, Sri Sulistijowati Handajani, and Titin Sri Martini. "Bootstrap Residual Ensemble Methods for Estimation of Standard Error of Parameter Logistic Regression To Hypercolesterolemia Patient Data In Health Laboratory Yogyakarta." Indonesian Journal of Applied Statistics 1, no. 1 (September 19, 2018): 29. http://dx.doi.org/10.13057/ijas.v1i1.24086.

Full text

Abstract:

Logistic regression is one of regression analysis to determine the relationship between response variable that have two possible values and some predictor variables. The method used to estimate logistic regression parameters is the maximum likelihood estimation (MLE) method. This method will produce a good estimate of the parameters if the estimation results have a small standard error.<br />In a research, the characteristics of good data must be representative of the population. If the samples taken in small size they will cause a large standard error value. Bootstrap is a resampling method that can be used to obtain a good estimate based on small data samples. Small data will be resampling so it can represent the population to obtain minimum standard error. Previous studies have discussed resampling bootstrap on residuals as much as b times. In this research we will be analyzed resampling bootstrap on the error added to the dependent variable and take the average parameter estimation ensemble logistic regression model resampling result. Next we calculate the standard value error logistic regression parameters bootstrap results.<br />This method is applied to the hypercholesterolemic patient status data in Health Laboratory Yogyakarta and after bootstrapping, the standard error produced is smaller than before the bootstrap resampling.<br />Keywords : logistic regression, standard error, bootstrap resampling, parameter estimation ensemble

APA, Harvard, Vancouver, ISO, and other styles

3

Naik, Bhaven, Laurence R. Rilett, Justice Appiah, and Lubinda F. Walubita. "Resampling Methods for Estimating Travel Time Uncertainty: Application of the Gap Bootstrap." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 42 (August 23, 2018): 137–47. http://dx.doi.org/10.1177/0361198118792124.

Full text

Abstract:

To a large extent, methods of forecasting travel time have placed emphasis on the quality of the forecasted value—how close is the forecast point estimate of the mean travel time to its respective field value? However, understanding the reliability or uncertainty margin that exists around the forecasted point estimate is also important. Uncertainty about travel time is a fundamental factor as it leads end-users to change their routes and schedules even when the average travel time is low. Statistical resampling methods have been used previously for uncertainty modeling within the travel time prediction environment. This paper applies a recently developed nonparametric resampling method, the gap bootstrap, to the travel time uncertainty estimation problem, especially as it pertains to large (probe) data sets for which common resampling methods may not be practical because of the possible computational burden and complex patterns of inhomogeneity. The gap bootstrap partitions the original data into smaller groups of approximately uniform data sets and recombines individual group uncertainty estimates into a single estimate of uncertainty. Results of the gap bootstrap uncertainty estimates are compared with those of two popular resampling methods—the traditional bootstrap and the block bootstrap. The results suggest that, for the datasets used in this research, the gap bootstrap adequately captures the dependent structure when compared with the traditional and block bootstrap methods and may thus yield more credible estimates of uncertainty than either the block bootstrap method or the traditional bootstrap method.

APA, Harvard, Vancouver, ISO, and other styles

4

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 (March 16, 2020): 579–89. http://dx.doi.org/10.1108/ijsi-10-2019-0108.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

Kashani, M., M. Arashi, and M. R. Rabiei. "Resampling in Fuzzy Regression via Jackknife-after-Bootstrap (JB)." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 04 (August 2021): 517–35. http://dx.doi.org/10.1142/s0218488521500227.

Full text

Abstract:

In fuzzy regression modeling, when the sample size is small, resampling methods are appropriate and useful for improving model estimation. However, in the commonly used bootstrap method, the standard errors of estimates are also random because of randomness existing in samples. This paper investigates the use of Jackknife-after-Bootstrap (JB) in fuzzy regression modeling to address this problem and produce estimates with smaller mean prediction errors. Performance analysis is carried out through some numerical illustrations and some interactive graphs to illustrate the superiority of the JB method compared to the bootstrap. Moreover, it is demonstrated that using the JB method, we have a significant model, with some sense; however, this is not the case using the bootstrap method.

APA, Harvard, Vancouver, ISO, and other styles

6

Fitrianto, Anwar, and Punitha Linganathan. "Comparisons between Resampling Techniques in Linear Regression: A Simulation Study." CAUCHY: Jurnal Matematika Murni dan Aplikasi 7, no. 3 (October 11, 2022): 345–53. http://dx.doi.org/10.18860/ca.v7i3.14550.

Full text

Abstract:

The classic methods used in estimating the parameters in linear regression need to fulfill some assumptions. If the assumptions are not fulfilled, the conclusion is questionable. Resampling is one of the ways to avoid such problems. The study aims to compare resampling techniques in linear regression. The original data used in the study is clean, without any influential observations, outliers and leverage points. The ordinary least square method was used as the primary method to estimate the parameters and then compared with resampling techniques. The variance, p-value, bias, and standard error are used as a scale to estimate the best method among random bootstrap, residual bootstrap and delete-one Jackknife. After all the analysis took place, it was found that random bootstrap did not perform well while residual and delete-one Jackknife works quite well. Random bootstrap, residual bootstrap, and Jackknife estimate better than ordinary least square. Is was found that residual bootstrap works well in estimating the parameter in the small sample. At the same time, it is suggested to use Jackknife when the sample size is big because Jackknife is more accessible to apply than residual bootstrap and Jackknife works well when the sample size is big.

APA, Harvard, Vancouver, ISO, and other styles

7

Ivšinović, Josip, and Nikola Litvić. "Application of the bootstrap method on a large input data set - case study western part of the Sava Depression." Rudarsko-geološko-naftni zbornik 36, no. 5 (2021): 13–19. http://dx.doi.org/10.17794/rgn.2021.5.2.

Full text

Abstract:

The bootstrap method is a nonparametric statistical method that provides through resampling the input data set to obtain a new data set that is normally distributed. Due to various factors, deep geological data are difficult to obtain many data set, and in most cases, they are not normally distributed. Therefore, it is necessary to introduce a statistical tool that will enable obtaining a set with which statistical analyses can be done. The bootstrap method was applied to field "A", reservoir "L" located in the western part of the Sava Depression. It was applied to the geological variable of porosity on a set of 25 data. The minimum number of resampling's required for a large sample to obtain a normal distribution is 1000. Interval estimation of porosity for reservoir "L" obtained by bootstrap method is 0.1875 to 0.2144 with 95% confidence level.

APA, Harvard, Vancouver, ISO, and other styles

8

Hung, Wen-Liang, E. Stanley Lee, and Shun-Chin Chuang. "Balanced bootstrap resampling method for neural model selection." Computers & Mathematics with Applications 62, no. 12 (December 2011): 4576–81. http://dx.doi.org/10.1016/j.camwa.2011.10.039.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Dwornicka, Renata, Andrii Goroshko, and Jacek Pietraszek. "The Smoothed Bootstrap Fine-Tuning." System Safety: Human - Technical Facility - Environment 1, no. 1 (March 1, 2019): 716–23. http://dx.doi.org/10.2478/czoto-2019-0091.

Full text

Abstract:

AbstractThe bootstrap method is a well-known method to gather a full probability distribution from the dataset of a small sample. The simple bootstrap i.e. resampling from the raw dataset often leads to a significant irregularities in a shape of resulting empirical distribution due to the discontinuity of a support. The remedy for these irregularities is the smoothed bootstrap: a small random shift of source points before each resampling. This shift is controlled by specifically selected distributions. The key issue is such parameter settings of these distributions to achieve the desired characteristics of the empirical distribution. This paper describes an example of this procedure.

APA, Harvard, Vancouver, ISO, and other styles

10

HE, XUMING, and FEIFANG HU. "SOME RECENT ADVANCES ON BOOTSTRAP." COSMOS 01, no. 01 (May 2005): 75–86. http://dx.doi.org/10.1142/s021960770500005x.

Full text

Abstract:

The bootstrap is a computer-based resampling method that can provide good approximations to the distribution of a given statistic. We review some common forms of bootstrap-based confidence intervals, with emphasis on some recent work on the estimating function bootstrap and Markov chain marginal bootstrap.

APA, Harvard, Vancouver, ISO, and other styles

11

Yu, Jie, Yu Mei Song, Shuang Yu, Ting Ting Wang, Qiao Chan Li, Li Da Sun, and Zhan Guo Li. "The Application of the Improved Bootstrap Method in the Verification of Maintainability Indicators of CNC Machine Tools." Advanced Materials Research 655-657 (January 2013): 1232–36. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.1232.

Full text

Abstract:

Bootstrap Method is commonly used in statistical inference and is convenient and practical in data processing. The paper puts forward an improved Bootstrap method which expands the range of resampling procedure to verify the maintenance indicators with a few test samples.

APA, Harvard, Vancouver, ISO, and other styles

12

Hall, M. J., H. F. P. van den Boogaard, R. C. Fernando, and A. E. Mynett. "The construction of confidence intervals for frequency analysis using resampling techniques." Hydrology and Earth System Sciences 8, no. 2 (April 30, 2004): 235–46. http://dx.doi.org/10.5194/hess-8-235-2004.

Full text

Abstract:

Abstract. Resampling techniques such as the Bootstrap and the Jack-knife are generic methods for the estimation of uncertainties in statistics. When applied in frequency analysis, resampling techniques can provide estimates of the uncertainties in both distribution parameters and quantile estimates in circumstances in which confidence limits cannot be obtained theoretically. Test experiments using two different parameter estimation methods on two types of distributions with different initial sample sizes and numbers of resamples has confirmed the utility of such methods. However, care is necessary in evaluating the skewness of the resampled quantiles, especially with small initial sample sizes. Keywords: Bootstrap, Jack-knife, frequency analysis, maximum likelihood method, maximum product of spacings method

APA, Harvard, Vancouver, ISO, and other styles

13

Zhen, Jiaqi. "Detection of Wideband Signal Number Based on Bootstrap Resampling." International Journal of Antennas and Propagation 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3856727.

Full text

Abstract:

Knowing source number correctly is the precondition for most spatial spectrum estimation methods; however, many snapshots are needed when we determine number of wideband signals. Therefore, a new method based on Bootstrap resampling is proposed in this paper. First, signals are divided into some nonoverlapping subbands; apply coherent signal methods (CSM) to focus them on the single frequency. Then, fuse the eigenvalues with the corresponding eigenvectors of the focused covariance matrix. Subsequently, use Bootstrap to construct the new resampling matrix. Finally, the number of wideband signals can be calculated with obtained vector sequences according to clustering technique. The method has a high probability of success under low signal to noise ratio (SNR) and small number of snapshots.

APA, Harvard, Vancouver, ISO, and other styles

14

KUMALASARI, NI LUH PUTU RATNA, NI LUH PUTU SUCIPTAWATI, and MADE SUSILAWATI. "PERBANDINGAN METODE MCD-BOOTSTRAP DAN LAD-BOOTSTRAP DALAM MENGATASI PENGARUH PENCILAN PADA ANALISIS REGRESI LINEAR BERGANDA." E-Jurnal Matematika 6, no. 1 (January 20, 2017): 47. http://dx.doi.org/10.24843/mtk.2017.v06.i01.p147.

Full text

Abstract:

Outliers are observations that are far away from other observations. Outlier can be interfered with the process of data analysis which influence the regression parameters estimation. Methods that are able to deal with outliers are Minimum Covariance Determinant and Least Absolute Deviation methods. However, if both methods are applied with small sample the validity of both methods is being questioned. This research applies bootstrap to MCD and LAD methods to small sample. Resampling using 500, 750,and 1000 with confidence interval of 95% and 99% shows that both methods produce an unbiased estimators at 10%, 15%, and 20% outliers. The confidence interval of MCD-Bootstrap method is shorter than LAD-Bootstrap method. Both are, MCD-Bootstrap method is a better thus than LAD-Bootstrap method.

APA, Harvard, Vancouver, ISO, and other styles

15

Putri, Yuli Eka, Kusman Sadik, and Cici Suhaeni. "Perbandingan Metode Dalil Limit Pusat Transformasi dan Resampling Bootstrap dalam Pembentukan Selang Kepercayaan." Xplore: Journal of Statistics 2, no. 2 (August 31, 2018): 73. http://dx.doi.org/10.29244/xplore.v2i2.108.

Full text

Abstract:

YULI EKA PUTRI. A Comparative Study of Central Limit Theorem, Transformation and Bootstrap Resampling in Determining Confidence Interval. Supervised by KUSMAN SADIK and CICI SUHAENI. The confidence interval is usually established under normality assumption. But, many real-life data does not belong to normal distribution. Many of them are skewed, such as chi-square distribution, generalized extreme value (GEV) or other distribution. For such data, we can use central limit theorem, transformation and bootstrap resampling method to construct confidence intervals. The performance of the methods in constructing the interval can be evaluated using confidence interval accuracy value, interval width, and standard deviation of the interval width. Thus we can determine the best method. The method is determined for having better performance if it has higher accuracy value, smaller interval width, and smaller standard deviation of interval width.This research use both simulated and real-life data. Simulated data is generated from the chi-square distribution, GEV and modified non-normal distribution. The modified non-normal distributed data is a modification of normal distributed data using quadratic and logaritm transformation. So that the data is no longer normally distributed. The results show that transformation method is well used for small sample sizes. Bootstrap resampling dan central limit theorem are better used for large sample sizes.

APA, Harvard, Vancouver, ISO, and other styles

16

Ghasemi, Reza, Samuel Morillas, Ahmad Nezakati, and Mohammadreza Rabiei. "Image Noise Reduction by Means of Bootstrapping-Based Fuzzy Numbers." Applied Sciences 12, no. 19 (September 21, 2022): 9445. http://dx.doi.org/10.3390/app12199445.

Full text

Abstract:

Removing or reducing noise in color images is one of the most important functions of image processing, which is used in many sciences. In many cases, nonlinear methods significantly reduce the noise in the image and are widely used today. One of these methods is the use of fuzzy logic. In this paper, we want to introduce a fuzzy filter by using the fuzzy metric for fuzzy sets. For this purpose, we define fuzzy color pixels by using the mean of neighborhoods. Due to the noise in the image, we use the bootstrap resampling method to reduce the effect of outliers. The concept of the strong law of large numbers for the bootstrap mean in fuzzy metric space helps us to use the resampling method.

APA, Harvard, Vancouver, ISO, and other styles

17

Gultom, Fandi Rezian Pratama, Solimun Solimun, and Nurjannah Nurjannah. "Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration." JTAM (Jurnal Teori dan Aplikasi Matematika) 6, no. 4 (October 7, 2022): 810. http://dx.doi.org/10.31764/jtam.v6i4.8617.

Full text

Abstract:

Soybean plants have limited growth with a planting period of 12 weeks, which causes the observed sample to be very small. A small sample of soybean plant growth observations can be bias causes in the conclusion of prediction results on soybean plant growth. The purpose this study is to apply the bootstrap resampling technique in Gompertz growth model which overcomes residual distribution with small samples, the research data was taken from soybean plant growth in four varieties with four spacing treatments, five replications and twelve weeks (long planting period). Gompertz growth model uses nonlinear least squares method in estimating parameters with Levenberg–Marquardt iteration. The value of the Gompertz model after resampling bootstrap has no significant difference. The adjusted R2 value of 0.96 is close to 1. This means that the total diversity of plant heights can be explained by the Gompertz model of 96 percent. Judging from the graph of predictions of soybean plant growth before resampling and after resampling coincide with each other it can also be seen in the initial growth values before resampling 14, 05 and 14.18, the maximum growth values are 55.13 and 55.60. Bootsrap resampling technique can overcome residual normality in the Gompertz growth model, but does not change the information in the initial data.

APA, Harvard, Vancouver, ISO, and other styles

18

Park, Jinsoo. "Resampling Method for Autocorrelated Bivariate Data Applying Modified Threshold Bootstrap." Journal of Next-generation Convergence Technology Association 4, no. 5 (October 31, 2020): 501–8. http://dx.doi.org/10.33097/jncta.2020.04.05.501.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Chernick, Michael R. "The jackknife: a resampling method with connections to the bootstrap." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 2 (December 8, 2011): 224–26. http://dx.doi.org/10.1002/wics.202.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Turkheimer, Federico, Louis Sokoloff, Alessandra Bertoldo, Giovanni Lucignani, Martin Reivich, Jurg L. Jaggi, and Kathleen Schmidt. "Estimation of Component and Parameter Distributions in Spectral Analysis." Journal of Cerebral Blood Flow & Metabolism 18, no. 11 (November 1998): 1211–22. http://dx.doi.org/10.1097/00004647-199811000-00007.

Full text

Abstract:

A method is presented for estimating the distributions of the components and parameters determined with spectral analysis when it is applied to a single data set. The method uses bootstrap resampling to simulate the effect of noise on the computed spectrum and to correct for possible bias in the estimates. A number of bootstrap procedures are reviewed, and one is selected for application to the kinetic analysis of positron emission tomography dynamic studies. The technique is shown to require minimal assumptions about noise in the measurements, and its small sample properties are established through Monte-Carlo simulations. The advantages and limitations of spectral analysis with bootstrap resampling for deriving inferences for tracer kinetic modeling are illustrated through sample analyses of time-activity curves for [18F]fluorodeoxyglucose and [15O]-labeled water.

APA, Harvard, Vancouver, ISO, and other styles

21

Okamoto, Shogo. "Bootstrap Resampling of Temporal Dominance of Sensations Curves to Compute Uncertainties." Foods 10, no. 10 (October 15, 2021): 2472. http://dx.doi.org/10.3390/foods10102472.

Full text

Abstract:

In the last decade, temporal dominance of sensations (TDS) methods have proven to be potent approaches in the field of food sciences. Accordingly, thus far, methods for analyzing TDS curves, which are the major outputs of TDS methods, have been developed. This study proposes a method of bootstrap resampling for TDS tasks. The proposed method enables the production of random TDS curves to estimate the uncertainties, that is, the 95% confidence interval and standard error of the curves. Based on Monte Carlo simulation studies, the estimated uncertainties are considered valid and match those estimated by approximated normal distributions with the number of independent TDS tasks or samples being 50–100 or greater. The proposed resampling method enables researchers to apply statistical analyses and machine-learning approaches that require a large sample size of TDS curves.

APA, Harvard, Vancouver, ISO, and other styles

22

Wang, Wei, Ahmad Hejasebazzi, Julia Zheng, and Kevin J. Liu. "Build a better bootstrap and the RAWR shall beat a random path to your door: phylogenetic support estimation revisited." Bioinformatics 37, Supplement_1 (July 1, 2021): i111—i119. http://dx.doi.org/10.1093/bioinformatics/btab263.

Full text

Abstract:

Abstract Motivation The standard bootstrap method is used throughout science and engineering to perform general-purpose non-parametric resampling and re-estimation. Among the most widely cited and widely used such applications is the phylogenetic bootstrap method, which Felsenstein proposed in 1985 as a means to place statistical confidence intervals on an estimated phylogeny (or estimate ‘phylogenetic support’). A key simplifying assumption of the bootstrap method is that input data are independent and identically distributed (i.i.d.). However, the i.i.d. assumption is an over-simplification for biomolecular sequence analysis, as Felsenstein noted. Results In this study, we introduce a new sequence-aware non-parametric resampling technique, which we refer to as RAWR (‘RAndom Walk Resampling’). RAWR consists of random walks that synthesize and extend the standard bootstrap method and the ‘mirrored inputs’ idea of Landan and Graur. We apply RAWR to the task of phylogenetic support estimation. RAWR’s performance is compared to the state-of-the-art using synthetic and empirical data that span a range of dataset sizes and evolutionary divergence. We show that RAWR support estimates offer comparable or typically superior type I and type II error compared to phylogenetic bootstrap support. We also conduct a re-analysis of large-scale genomic sequence data from a recent study of Darwin’s finches. Our findings clarify phylogenetic uncertainty in a charismatic clade that serves as an important model for complex adaptive evolution. Availability and implementation Data and software are publicly available under open-source software and open data licenses at: https://gitlab.msu.edu/liulab/RAWR-study-datasets-and-scripts.

APA, Harvard, Vancouver, ISO, and other styles

23

Vrigazova, Borislava. "The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems." Business Systems Research Journal 12, no. 1 (May 1, 2021): 228–42. http://dx.doi.org/10.2478/bsrj-2021-0015.

Full text

Abstract:

Abstract Background: The bootstrap can be alternative to cross-validation as a training/test set splitting method since it minimizes the computing time in classification problems in comparison to the tenfold cross-validation. Objectives: Тhis research investigates what proportion should be used to split the dataset into the training and the testing set so that the bootstrap might be competitive in terms of accuracy to other resampling methods. Methods/Approach: Different train/test split proportions are used with the following resampling methods: the bootstrap, the leave-one-out cross-validation, the tenfold cross-validation, and the random repeated train/test split to test their performance on several classification methods. The classification methods used include the logistic regression, the decision tree, and the k-nearest neighbours. Results: The findings suggest that using a different structure of the test set (e.g. 30/70, 20/80) can further optimize the performance of the bootstrap when applied to the logistic regression and the decision tree. For the k-nearest neighbour, the tenfold cross-validation with a 70/30 train/test splitting ratio is recommended. Conclusions: Depending on the characteristics and the preliminary transformations of the variables, the bootstrap can improve the accuracy of the classification problem.

APA, Harvard, Vancouver, ISO, and other styles

24

DAYANTI, NI PUTU IIN VINNY, NI LUH PUTU SUCIPTAWATI, and MADE SUSILAWATI. "PENERAPAN BOOTSTRAP DALAM METODE MINIMUM COVARIANCE DETERMINANT (MCD) DAN LEAST MEDIAN OF SQUARES (LMS) PADA ANALISIS REGRESI LINIER BERGANDA." E-Jurnal Matematika 5, no. 1 (January 30, 2016): 22. http://dx.doi.org/10.24843/mtk.2016.v05.i01.p116.

Full text

Abstract:

Ordinary Least Squares (OLS) Method is a good method to estimate regression parameters when there is no violation in classical assumptions, such as the existence of outlier. Outliers can lead to biased parameters estimator, therefore we need a method that can may not affected by the existence of outlier such as Minimum Covariance Determinant (MCD) and Least Median of Squares (LMS). However, the application of this method is less accurate when it is used for small data. To overcome this problem, it was aplicated bootstrap method in MCD and LMS to determine the comparison of bias in parameters which were produced by both methods in dealing outlier in small data. The used bootstrap method in this study was the residual bootstrap that works by resampling the residuals. By using 95% and 99% confidence level and 5%, 10% and 15% outlier percentage, MCD-bootstrap and LMS-bootstrap give value of parameter estimators which were unbias for all percentage of outlier. We also found that the widht of range which produced by MCD-bootstrap method was shorter than LMS-bootstrap method produced. This indicates that MCD-bootstrap method was a better method than LMS-bootstrap method.

APA, Harvard, Vancouver, ISO, and other styles

25

Yauck, Mamadou, Erica E. M. Moodie, Herak Apelian, Alain Fourmigue, Daniel Grace, Trevor A. Hart, Gilles Lambert, and Joseph Cox. "Neighborhood Bootstrap for Respondent-Driven Sampling." Journal of Survey Statistics and Methodology 10, no. 2 (February 26, 2022): 419–38. http://dx.doi.org/10.1093/jssam/smab057.

Full text

Abstract:

Abstract Respondent-driven sampling (RDS) is a form of link-tracing sampling, a sampling technique used for “hard-to-reach” populations that aims to leverage individuals’ social relationships to reach potential participants. There is a growing interest in the estimation of uncertainty for RDS as recent findings suggest that most variance estimators underestimate variability. Recently, Baraff et al. proposed the tree bootstrap method based on resampling the RDS recruitment tree, and empirically showed that this method outperforms current bootstrap methods. However, some findings suggest that the tree bootstrap (severely) overestimates uncertainty. In this article, we propose the neighborhood bootstrap method for quantifying uncertainty in RDS. We prove the consistency of our method under some conditions and investigate its finite sample performance, through a simulation study, under realistic RDS sampling assumptions.

APA, Harvard, Vancouver, ISO, and other styles

26

Horowitz, Joel L. "Bootstrap Methods in Econometrics." Annual Review of Economics 11, no. 1 (August 2, 2019): 193–224. http://dx.doi.org/10.1146/annurev-economics-080218-025651.

Full text

Abstract:

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in the references that are cited.

APA, Harvard, Vancouver, ISO, and other styles

27

Prihatmono, Fajar, Moh Yamin Darsyah, and Abdul Karim. "RESIDUAL BOOTSTRAP RESAMPLING METHOD FOR MULTIPLE LINEAR REGRESSION MODEL PARAMETER ESTIMATION." Jurnal Litbang Edusaintech 1, no. 1 (December 23, 2020): 35–43. http://dx.doi.org/10.51402/jle.v1i1.8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Shen, Meiyu, and Stella G. Machado. "Bioequivalence evaluation of sparse sampling pharmacokinetics data using bootstrap resampling method." Journal of Biopharmaceutical Statistics 27, no. 2 (December 2016): 257–64. http://dx.doi.org/10.1080/10543406.2016.1265543.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Roy, Paramita, Subodh Chandra Pal, Alireza Arabameri, Rabin Chakrabortty, Biswajeet Pradhan, Indrajit Chowdhuri, Saro Lee, and Dieu Tien Bui. "Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility." Remote Sensing 12, no. 20 (October 10, 2020): 3284. http://dx.doi.org/10.3390/rs12203284.

Full text

Abstract:

The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and maintaining the correspondence between different spheres of the environment. The major goal of this study is to evaluate the gully erosion susceptibility in the rugged topography of the Hinglo River Basin of eastern India, which ultimately contributes to sustainable land management practices. Due to the nature of data instability, the weakness of the classifier andthe ability to handle data, the accuracy of a single method is not very high. Thus, in this study, a novel resampling algorithm was considered to increase the robustness of the classifier and its accuracy. Gully erosion susceptibility maps have been prepared using boosted regression trees (BRT), multivariate adaptive regression spline (MARS) and spatial logistic regression (SLR) with proposed resampling techniques. The re-sampling algorithm was able to increase the efficiency of all predicted models by improving the nature of the classifier. Each variable in the gully inventory map was randomly allocated with 5-fold cross validation, 10-fold cross validation, bootstrap and optimism bootstrap, while each consisted of 30% of the database. The ensemble model was tested using 70% and validated with the other 30% using the K-fold cross validation (CV) method to evaluate the influence of the random selection of training and validation database. Here, all resampling methods are associated with higher accuracy, but SLR bootstrap optimism is more optimal than any other methods according to its robust nature. The AUC values of BRT optimism bootstrap, MARS optimism bootstrap and SLR optimism bootstrap are 87.40%, 90.40% and 90.60%, respectively. According to the SLR optimism bootstrap, the 107,771 km2 (27.51%) area of this region is associated with a very high to high susceptible to gully erosion. This potential developmental area of the gully was found primarily in the Hinglo River Basin, where lateral exposure was mainly observed with scarce vegetation. The outcome of this work can help policy-makers to implement remedial measures to minimize the damage caused by erosion of the gully.

APA, Harvard, Vancouver, ISO, and other styles

30

Pelletier, Dominique, and Philippe Gros. "Assessing the impact of Sampling Error on Model-Based Management Advice: Comparison of Equilibrium Yield per Recruit Variance Estimators." Canadian Journal of Fisheries and Aquatic Sciences 48, no. 11 (November 1, 1991): 2129–39. http://dx.doi.org/10.1139/f91-252.

Full text

Abstract:

The assessment offish stocks often relies on data estimated from complex sampling schemes. For instance, catch and weight-at-age estimates result from sampling commercial landings. This paper studies the propagation of sampling error in catches in an equilibrium yield per recruit model. The covariance matrix of catch estimators is calculated for a given design using sampling theory. The impact of this uncertainty on estimated yield per recruit is assessed by three techniques: the delta method, trials from a multinormal distribution of catches (Gaussian approximation), and bootstrap. The comparison of results leads to the following conclusions, (i) For the model studied, the delta method probably overestimates the variance of the response, (ii) Gaussian approximation and bootstrap give similar values. As the latter is free of approximation and of restrictive assumptions, this suggests that the yield model chosen is to some extent insensitive to the distributional form of catch estimators, (iii) Bootstrap is the method of choice, provided that resampling accurately mimics the whole complexity of the original sampling design. If resampling is not properly achieved, variances could be severely underestimated.

APA, Harvard, Vancouver, ISO, and other styles

31

Cattaneo, Matias D., Michael Jansson, and Kenichi Nagasawa. "Bootstrap‐Based Inference for Cube Root Asymptotics." Econometrica 88, no. 5 (2020): 2203–19. http://dx.doi.org/10.3982/ecta17950.

Full text

Abstract:

This paper proposes a valid bootstrap‐based distributional approximation for M‐estimators exhibiting a Chernoff (1964)‐type limiting distribution. For estimators of this kind, the standard nonparametric bootstrap is inconsistent. The method proposed herein is based on the nonparametric bootstrap, but restores consistency by altering the shape of the criterion function defining the estimator whose distribution we seek to approximate. This modification leads to a generic and easy‐to‐implement resampling method for inference that is conceptually distinct from other available distributional approximations. We illustrate the applicability of our results with four examples in econometrics and machine learning.

APA, Harvard, Vancouver, ISO, and other styles

32

Hermawan, Toto. "APLIKASI BOOTSTRAP PADA ANALISIS REGRESI UNTUK DATA KECELAKAAN KERJA." Academy of Education Journal 10, no. 01 (January 7, 2019): 55–62. http://dx.doi.org/10.47200/aoej.v10i01.271.

Full text

Abstract:

To find out the relationship between two or more variables, regression analysis can be used. The definition of regression analysis itself is a data analysis method that utilizes the relationship between two or more variables. One concern in regression analysis is one of them is the standard error of estimation of the regression coefficient. In a regression there is already a formula for estimating standard errors. In addition, the standard error can also be estimated by the resampling method, which is bootstrap. Bootstrapping is very useful as an alternative to estimating parameters or standard errors when researchers feel hesitant to meet the assumptions in their data, for example the data are not normally distributed. In addition, bootstrapping is also useful when parametric inference requires a very complicated formula for calculating standard errors (Widhiarso, 2012). In this paper we will compare the standard error estimates obtained through existing formulas with the standard error estimates obtained through bootstrap resampling.

APA, Harvard, Vancouver, ISO, and other styles

33

Febriady, Mukhlis, Samsuryadi Samsuryadi, and Dian Palupi Rini. "Klasifikasi Transaksi Penipuan Pada Kartu Kredit Menggunakan Metode Resampling Dan Pembelajaran Mesin." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 2 (April 25, 2022): 1010. http://dx.doi.org/10.30865/mib.v6i2.3515.

Full text

Abstract:

The high number of credit card fraud causes a lot of losses for both users and credit service providers. Because the rate of credit card transactions is very fast, it is necessary to detect credit card fraud as early as possible. However, another challenge that is no less important is the amount of data that is imbalanced between valid and invalid transactions. One solution to the problem of data imbalance is to use a resampling method that can improve the quantity of data so that the accuracy results are good. In this study, three types of resampling methods were implemented, SMOTE, bootstrap, and jackknife. Furthermore, to validate the success of the resampling method, three types of machine learning methods were used. The machine learning methods are SVM, ANN, and random forest. From the test results, it was found that the combination of resampling SMOTE and random forest methods produced the best performance with values of accuracy, precision, recall and F1-score of 99.95%, 81.63%, 90.91% and 86.02%, respectively.

APA, Harvard, Vancouver, ISO, and other styles

34

Lee, D., J. K. Kim, and C. J. Skinner. "Within-cluster resampling for multilevel models under informative cluster size." Biometrika 106, no. 4 (July 23, 2019): 965–72. http://dx.doi.org/10.1093/biomet/asz035.

Full text

Abstract:

Summary A within-cluster resampling method is proposed for fitting a multilevel model in the presence of informative cluster size. Our method is based on the idea of removing the information in the cluster sizes by drawing bootstrap samples which contain a fixed number of observations from each cluster. We then estimate the parameters by maximizing an average, over the bootstrap samples, of a suitable composite loglikelihood. The consistency of the proposed estimator is shown and does not require that the correct model for cluster size is specified. We give an estimator of the covariance matrix of the proposed estimator, and a test for the noninformativeness of the cluster sizes. A simulation study shows, as in Neuhaus & McCulloch (2011), that the standard maximum likelihood estimator exhibits little bias for some regression coefficients. However, for those parameters which exhibit nonnegligible bias, the proposed method is successful in correcting for this bias.

APA, Harvard, Vancouver, ISO, and other styles

35

Sacha, Varin, and Demosthenes B. Panagiotakos. "Insights in Hypothesis Testing and Making Decisions in Biomedical Research." Open Cardiovascular Medicine Journal 10, no. 1 (September 30, 2016): 196–200. http://dx.doi.org/10.2174/1874192401610010196.

Full text

Abstract:

It is a fact that p values are commonly used for inference in biomedical and other social fields of research. Unfortunately, the role of p value is very often misused and misinterpreted; that is why it has been recommended the use of resampling methods, like the bootstrap method, to calculate the confidence interval, which provides more robust results for inference than does p value. In this review a discussion is made about the use of p values through hypothesis testing and its alternatives using resampling methods to develop confidence intervals of the tested statistic or effect measure.

APA, Harvard, Vancouver, ISO, and other styles

36

ASTARI, NI MADE METTA, NI LUH PUTU SUCIPTAWATI, and I. KOMANG GDE SUKARSA. "PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI." E-Jurnal Matematika 3, no. 4 (November 28, 2014): 130. http://dx.doi.org/10.24843/mtk.2014.v03.i04.p075.

Full text

Abstract:

Statistical analysis which aims to analyze a linear relationship between the independent variable and the dependent variable is known as regression analysis. To estimate parameters in a regression analysis method commonly used is the Ordinary Least Square (OLS). But the assumption is often violated in the OLS, the assumption of normality due to one outlier. As a result of the presence of outliers is parameter estimators produced by the OLS will be biased. Bootstrap Residual is a bootstrap method that is applied to the residual resampling process. The results showed that the residual bootstrap method is only able to overcome the bias on the number of outliers 5% with 99% confidence intervals. The resulting parameters estimators approach the residual bootstrap values ??OLS initial allegations were also able to show that the bootstrap is an accurate prediction tool.

APA, Harvard, Vancouver, ISO, and other styles

37

Quatember, Andreas. "The Finite Population Bootstrap - From the Maximum Likelihood to the Horvitz-Thompson Approach." Austrian Journal of Statistics 43, no. 2 (June 11, 2014): 93–102. http://dx.doi.org/10.17713/ajs.v43i2.10.

Full text

Abstract:

The finite population bootstrap method is used as a computer-intensive alternative to estimate the sampling distribution of a sample statis-tic. The generation of a so-called “bootstrap population” is the necessarystep between the original sample drawn and the resamples needed to mimicthis distribution. The most important question for researchers to answer ishow to create an adequate bootstrap population, which may serve as a close-to-reality basis for the resampling process. In this paper, a review of someapproaches to answer this fundamental question is presented. Moreover, anapproach based on the idea behind the Horvitz-Thompson estimator allow-ing not only whole units in the bootstrap population but also parts of wholeunits is proposed. In a simulation study, this method is compared with a moreheuristic technique from the bootstrap literature.

APA, Harvard, Vancouver, ISO, and other styles

38

Kulkarni, Rishikesh U., Catherine L. Wang, and Carolyn R. Bertozzi. "Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance." PLOS Computational Biology 18, no. 5 (May 2, 2022): e1010061. http://dx.doi.org/10.1371/journal.pcbi.1010061.

Full text

Abstract:

While hierarchical experimental designs are near-ubiquitous in neuroscience and biomedical research, researchers often do not take the structure of their datasets into account while performing statistical hypothesis tests. Resampling-based methods are a flexible strategy for performing these analyses but are difficult due to the lack of open-source software to automate test construction and execution. To address this, we present Hierarch, a Python package to perform hypothesis tests and compute confidence intervals on hierarchical experimental designs. Using a combination of permutation resampling and bootstrap aggregation, Hierarch can be used to perform hypothesis tests that maintain nominal Type I error rates and generate confidence intervals that maintain the nominal coverage probability without making distributional assumptions about the dataset of interest. Hierarch makes use of the Numba JIT compiler to reduce p-value computation times to under one second for typical datasets in biomedical research. Hierarch also enables researchers to construct user-defined resampling plans that take advantage of Hierarch’s Numba-accelerated functions.

APA, Harvard, Vancouver, ISO, and other styles

39

Aziz, Okta Qomaruddin. "Performance improvement in Resampling Based Clustering." MATICS 12, no. 1 (April 7, 2020): 75. http://dx.doi.org/10.18860/mat.v12i1.8918.

Full text

Abstract:

Clustering is one of powerful technique to find a biological mechanism in gene expression. This technique identify a gene that has same expression. Using bootstrap method we can improve the quality of microarray, thus resampling based clustering (RC) is consider one of the improvement. RC use K-means clustering to determine initial parameter and need thousands of iteration to converge. Performance improvement can be done at preprocess, such as normalization and changing the initial parameter. Normalization can remove or lower the bias in microarray. The result show that normalization can improve the accuracy of RC. In addition, for parameter K, a lower value will lower the accuracy of this RC.

APA, Harvard, Vancouver, ISO, and other styles

40

Guan, Weihua. "From the Help Desk: Bootstrapped Standard Errors." Stata Journal: Promoting communications on statistics and Stata 3, no. 1 (March 2003): 71–80. http://dx.doi.org/10.1177/1536867x0300300105.

Full text

Abstract:

Bootstrapping is a nonparametric approach for evaluating the dis-tribution of a statistic based on random resampling. This article illustrates the bootstrap as an alternative method for estimating the standard errors when the theoretical calculation is complicated or not available in the current software.

APA, Harvard, Vancouver, ISO, and other styles

41

van den Boogaard, H. F. P., and M. J. Hall. "The construction of confidence intervals for frequency analysis using resampling techniques: a supplementary note." Hydrology and Earth System Sciences 8, no. 6 (December 31, 2004): 1174–78. http://dx.doi.org/10.5194/hess-8-1174-2004.

Full text

Abstract:

Abstract. In a recent contribution, Hall et al. (2004) examined the use of the Bootstrap resampling technique as a means of constructing confidence limits for the quantiles of the (two-parameter) Gumbel and the (three-parameter) Weibull distributions. Particular emphasis was placed on the behaviour of sample sizes of the order of 30, which are typical of those encountered in hydrological frequency analysis. The resampled confidence limits obtained for the Gumbel distribution were found to be comparable with those based upon a well-known theoretical approximation. However, those for samples of size 30 from the Weibull distribution were shown to be more problematical, with the results dependent upon the skewnesses of the resampled distributional parameters. For a further and more quantitative assessment of the suitability of Bootstrap resampling for constructing confidence intervals, so-called coverage rates were evaluated for the Weibull distribution in a supplementary study. The results show a satisfactory performance when using the percentile method but do not really mitigate the conclusion of the original study that resampled confidence limits should be employed with caution when sample sizes are of the order of 30. Keywords: Bootstrap, Jack-knife, frequency analysis, maximum likelihood method, maximum product of spacings method, confidence intervals, coverage rates

APA, Harvard, Vancouver, ISO, and other styles

42

Franceschi, Sara, Rosa Maria Di Biase, Agnese Marcelli, and Lorenzo Fattorini. "Some Empirical Results on Nearest-Neighbour Pseudo-populations for Resampling from Spatial Populations." Stats 5, no. 2 (April 15, 2022): 385–400. http://dx.doi.org/10.3390/stats5020022.

Full text

Abstract:

In finite populations, pseudo-population bootstrap is the sole method preserving the spirit of the original bootstrap performed from iid observations. In spatial sampling, theoretical results about the convergence of bootstrap distributions to the actual distributions of estimators are lacking, owing to the failure of spatially balanced sampling designs to converge to the maximum entropy design. In addition, the issue of creating pseudo.populations able to mimic the characteristics of real populations is challenging in spatial frameworks where spatial trends, relationships, and similarities among neighbouring locations are invariably present. In this paper, we propose the use of the nearest-neighbour interpolation of spatial populations for constructing pseudo-populations that converge to real populations under mild conditions. The effectiveness of these proposals with respect to traditional pseudo-populations is empirically checked by a simulation study.

APA, Harvard, Vancouver, ISO, and other styles

43

Cooil, Bruce, Russell S. Winer, and David L. Rados. "Cross-Validation for Prediction." Journal of Marketing Research 24, no. 3 (August 1987): 271–79. http://dx.doi.org/10.1177/002224378702400303.

Full text

Abstract:

The authors review and critically evaluate three major approaches to cross-validation in the context of predictive validity: (1) sample-splitting, (2) resampling plans such as bootstrapping, and (3) a method that simultaneously estimates parameters and cross-validates. Because of the information loss involved and the availability of shrinkage formulas, they argue that data-splitting is inferior to the simultaneous approach. An empirical example illustrates how the simultaneous approach can be used in conjunction with bootstrap resampling to construct prediction intervals that are superior to classical prediction intervals.

APA, Harvard, Vancouver, ISO, and other styles

44

Hu, Yi-Ming, Zhong-Min Liang, Bin-Quan Li, and Zhong-Bo Yu. "Uncertainty Assessment of Hydrological Frequency Analysis Using Bootstrap Method." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/724632.

Full text

Abstract:

The hydrological frequency analysis (HFA) is the foundation for the hydraulic engineering design and water resources management. Hydrological extreme observations or samples are the basis for HFA; the representativeness of a sample series to the population distribution is extremely important for the estimation reliability of the hydrological design value or quantile. However, for most of hydrological extreme data obtained in practical application, the size of the samples is usually small, for example, in China about 40~50 years. Generally, samples with small size cannot completely display the statistical properties of the population distribution, thus leading to uncertainties in the estimation of hydrological design values. In this paper, a new method based on bootstrap is put forward to analyze the impact of sampling uncertainty on the design value. By bootstrap resampling technique, a large number of bootstrap samples are constructed from the original flood extreme observations; the corresponding design value or quantile is estimated for each bootstrap sample, so that the sampling distribution of design value is constructed; based on the sampling distribution, the uncertainty of quantile estimation can be quantified. Compared with the conventional approach, this method provides not only the point estimation of a design value but also quantitative evaluation on uncertainties of the estimation.

APA, Harvard, Vancouver, ISO, and other styles

45

Kim, Joseph H. T., and Mary R. Hardy. "Estimating the Variance of Bootstrapped Risk Measures." ASTIN Bulletin 39, no. 1 (May 2009): 199–223. http://dx.doi.org/10.2143/ast.39.1.2038062.

Full text

Abstract:

AbstractIn Kim and Hardy (2007) the exact bootstrap was used to estimate certain risk measures including Value at Risk and the Conditional Tail Expectation. In this paper we continue this work by deriving the influence function of the exact-bootstrapped quantile risk measure. We can use the influence function to estimate the variance of the exact-bootstrap risk measure. We then extend the result to the L-estimator class, which includes the conditional tail expectation risk measure. The resulting formula provides an alternative way to estimate the variance of the bootstrapped risk measures, or the whole L-estimator class in an analytic form. A simulation study shows that this new method is comparable to the ordinary resampling-based bootstrap method, with the advantages of an analytic approach.

APA, Harvard, Vancouver, ISO, and other styles

46

Antal, Erika, and Yves Tillé. "A new resampling method for sampling designs without replacement: the doubled half bootstrap." Computational Statistics 29, no. 5 (May 8, 2014): 1345–63. http://dx.doi.org/10.1007/s00180-014-0495-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Nemec, A. F. L., and R. O. Brinkhurst. "Using the Bootstrap to Assess Statistical Significance in the Cluster Analysis of Species Abundance Data." Canadian Journal of Fisheries and Aquatic Sciences 45, no. 6 (June 1, 1988): 965–70. http://dx.doi.org/10.1139/f88-118.

Full text

Abstract:

Clustering techniques are frequently used to analyze species abundance data, despite a lack of objective criteria for assessing the results of such analyses. We show that a nonparametric statistical technique known as the "bootstrap" can, in certain circumstances, be used to overcome this deficiency. The bootstrap uses the distributional properties of a "bootstrap sample", i.e. a sample obtained by an appropriate resampling of the data, to make statistical inferences about the underlying population. The method is versatile and can be readily applied to complex hypothesis testing problems, since the statistical properties of a bootstrap sample can always be determined by simulation. To illustrate the application of the bootstrap to the cluster analysis of ecological data, we derive a test for the "statistical significance" of clusters of communities and show that two dendrograms can be compared by bootstrapping the Fowlkes–Mallow statistic.

APA, Harvard, Vancouver, ISO, and other styles

48

Hartono, Hartono, and Erianto Ongko. "Avoiding Overfitting dan Overlapping in Handling Class Imbalanced Using Hybrid Approach with Smoothed Bootstrap Resampling and Feature Selection." JOIV : International Journal on Informatics Visualization 6, no. 2 (June 28, 2022): 343. http://dx.doi.org/10.30630/joiv.6.2.985.

Full text

Abstract:

The dataset tends to have the possibility to experience imbalance as indicated by the presence of a class with a much larger number (majority) compared to other classes(minority). This condition results in the possibility of failing to obtain a minority class even though the accuracy obtained is high. In handling class imbalance, the problems of diversity and classifier performance must be considered. Hence, the Hybrid Approach method that combines the sampling method and classifier ensembles presents satisfactory results. The Hybrid Approach generally uses the oversampling method, which is prone to overfitting problems. The overfitting condition is indicated by high accuracy in the training data, but the testing data can show differences in accuracy. Therefore, in this study, Smoothed Bootstrap Resampling is the oversampling method used in the Hybrid Approach, which can prevent overfitting. However, it is not only the class imbalance that contributes to the decline in classifier performance. There are also overlapping issues that need to be considered. The approach that can be used to overcome overlapping is Feature Selection. Feature selection can reduce overlap by minimizing the overlap degree. This research combined the application of Feature Selection with Hybrid Approach Redefinition, which modifies the use of Smoothed Bootstrap Resampling in handling class imbalance in medical datasets. The preprocessing stage in the proposed method was carried out using Smoothed Bootstrap Resampling and Feature Selection. The Feature Selection method used is Feature Assessment by Sliding Thresholds (FAST). While the processing is done using Random Under Sampling and SMOTE. The overlapping measurement parameters use Augmented R-Value, and Classifier Performance uses the Balanced Error Rate, Precision, Recall, and F-Value parameters. The Balanced Error Rate states the combined error of the majority and minority classes in the 10-Fold Validation test, allowing each subset to become training data. The results showed that the proposed method provides better performance when compared to the comparison method

APA, Harvard, Vancouver, ISO, and other styles

49

Ghadhban, Ghufran A., and Huda A. Rasheed. "Robust Tests for the Mean Difference in Paired Data by Using Bootstrap Resampling Technique." Ibn AL- Haitham Journal For Pure and Applied Sciences 34, no. 3 (August 1, 2021): 73–86. http://dx.doi.org/10.30526/34.3.2680.

Full text

Abstract:

The paired sample t-test for testing the difference between two means in paired data is not robust against the violation of the normality assumption. In this paper, some alternative robust tests have been suggested by using the bootstrap method in addition to combining the bootstrap method with the W.M test. Monte Carlo simulation experiments were employed to study the performance of the test statistics of each of these three tests depending on type one error rates and the power rates of the test statistics. The three tests have been applied on different sample sizes generated from three distributions represented by Bivariate normal distribution, Bivariate contaminated normal distribution, and the Bivariate Exponential distribution.

APA, Harvard, Vancouver, ISO, and other styles

50

WALLING, GRANT A., PETER M. VISSCHER, and CHRIS S. HALEY. "A comparison of bootstrap methods to construct confidence intervals in QTL mapping." Genetical Research 71, no. 2 (April 1998): 171–80. http://dx.doi.org/10.1017/s0016672398003164.

Full text

Abstract:

The determination of empirical confidence intervals for the location of quantitative trait loci (QTLs) by interval mapping was investigated using simulation. Confidence intervals were created using a non-parametric (resampling method) and parametric (resimulation method) bootstrap for a backcross population derived from inbred lines. QTLs explaining 1%, 5% and 10% of the phenotypic variance were tested in populations of 200 or 500 individuals. Results from the two methods were compared at all locations along one half of the chromosome. The non-parametric bootstrap produced results close to expectation at all non-marker locations, but confidence intervals when the QTL was located at the marker were conservative. The parametric method performed poorly; results varied from conservative confidence intervals at the location of the marker, to anti-conservative intervals midway between markers. The results were shown to be influenced by a bias in the mapping procedure and by the accumulation of type 1 errors at the location of the markers. The parametric bootstrap is not a suitable method for constructing confidence intervals in QTL mapping. The confidence intervals from the non-parametric bootstrap are accurate and suitable for practical use.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Bootstrap resampling method'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles