Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods

Bootstrap methods: another look at the jackknife

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Continuous Univariate Distributions.

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The Influence Curve and Its Role in Robust Estimation

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The Jackknife Estimate of Variance

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The jackknife-a review

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Computers and the Theory of Statistics: Thinking the Unthinkable

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Censored Data and the Bootstrap

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Inference from Complex Samples

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Robust estimation and outlier detection with correlation coefficients

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Pseudo-Replication: Half Samples

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Bayesian Monte Carlo assimilation for the PETALE experimental programme using inter-dosimeter correlation

Laureau, A., Lamirand, V. P., Rochman, D. A., & Pautz, A. (2020) · EPJ Web of Conferences · doi:10.1051/epjconf/202023918004 · Cited by 6

A New Nonparametric Discriminant Analysis Algorithm Accounting for Bounded Data Errors

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Relations between cognitive status and medication adherence in patients treated for memory disorders

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Setting M-Estimation Parameters for Detection and Treatment of Influential Values

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The age and growth of hottentot seabream Pachymetopon blochii before and after the South African linefish state of emergency in 2000

Farthing, M., Winkler, A. C., Anderson, K., Kerwath, S., Wilke, C. G., & Potts, W. M. (2018) · African Journal of Marine Science · doi:10.2989/1814232x.2018.1475302 · Cited by 6

An enhanced statistical approach for evolutionary algorithm comparison

Carrano, E. G., Takahashi, R. H. C., & Wanner, E. F. (2008) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1389095.1389270 · Cited by 6

Age and growth of the queen mackerel Scomberomorus plurilineatus from KwaZulu-Natal, South Africa

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Age-Dependent Habitat Identification of Mediterranean Swordfish: Application on Commercial Fishery Data and Potential Use in Fisheries Management

Damalas, D., & Megalofonou, P. (2014) · Reviews in Fisheries Science & Aquaculture · doi:10.1080/23308249.2014.951987 · Cited by 7

Mineral resource estimation using weighted jackknife kriging

Bargawa, W. S. (2016) · AIP conference proceedings · doi:10.1063/1.4958541 · Cited by 6

The Effect of Cluster Sampling Design in Survey Research on the Standard Error Statistic.

Wang, L., & Fan, X. (1997) · Cited by 6

Nonparametric Estimates of Low BiasPreprint

Withers, C. S., & Nadarajah, S. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1008.0127 · Cited by 6

The Effect of Electricity Prices on Residential Solar Photovoltaic Panel Adoption: Fukushima as a Natural ExperimentPreprint

Chan, H. R., Kiso, T., & Arino, Y. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2918807 · Cited by 6

Being in the zone: Flow state and the underlying neural dynamics in video game playingPreprint

Yun, K., Doh, S., Carrus, E., Wu, D.-A. J., & Shinsuke Shimojo, A. (2017) · arXiv (Cornell University) · Cited by 6

AUC: Nonparametric Estimators and Their Smoothness.Preprint

Yousef, W. A. (2019) · arXiv (Cornell University) · Cited by 6

Markov State Model Approach to Simulate Self-Assembly

Trubiano, A., & Hagan, M. F. (2024) · Physical Review X · doi:10.1103/physrevx.14.041063 · Cited by 7

Robust Random Forest Model for Faulting Prediction in Jointed Concrete Pavement

Chen, Y., Ling, M., Lytton, R. L., & Xu, J. (2024) · Journal of Transportation Engineering Part B Pavements · doi:10.1061/jpeodx.pveng-1489 · Cited by 5

Seeking metal–organic frameworks for hydrogen storage using classical and quantum active learning

Lourenço, M. P., Shukla, R., Naseri, M., Gaur, D. R., Singh, U., Gusarov, S., & Salahub, D. R. (2025) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp02747k · Cited by 5

Spatial attention selectively alters visual cortical representation during target anticipation

Tünçok, E., Carrasco, M., & Winawer, J. A. (2025) · Nature Communications · doi:10.1038/s41467-025-63795-3 · Cited by 23

Inference on stress-strength reliability model under step-stress partially accelerated life testing based on the Lomax distribution

Hassan, A. S., & Elghaffar, A. M. A. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02648-7 · Cited by 5

On feature selection for supervised learning problems involving high-dimensional analytical information

Žuvela, P., & Liu, J. J. (2016) · RSC Advances · doi:10.1039/c6ra09336a · Cited by 5

An elastic net penalized small area model combining unit- and area-level data for regional hypertension prevalence estimation

Burgard, J. P., Krause, J., & Münnich, R. T. (2020) · Journal of Applied Statistics · doi:10.1080/02664763.2020.1765323 · Cited by 5

Non-homogeneous Poisson process intensity modeling and estimation using measure transport

Ng, T. L. J., & Zammit‐Mangion, A. (2022) · Bernoulli · doi:10.3150/22-bej1480 · Cited by 5

Efficiency of multinational banks: Impacts of geographic and product loci

Yildirim, C., Kasman, A., & Gulamhussen, M. A. (2023) · International Business Review · doi:10.1016/j.ibusrev.2023.102162 · Cited by 5

Large Deviations in Monte Carlo Methods

Andrea Pelissetto, C., & Ricci‐Tersenghi, F. (2014) · Lecture notes in physics · doi:10.1007/978-3-642-54251-0_6 · Cited by 5

Computer Program Exchange

Wolfe, E. W. (2008) · Applied Psychological Measurement · doi:10.1177/0146621607312308 · Cited by 5

A Statistical Model and Estimation of Disease Rates as Functions of Age and Time

Bell, B. M., & Flaxman, A. D. (2013) · SIAM Journal on Scientific Computing · doi:10.1137/120872413 · Cited by 5

A genetic programming approach for Burkholderia Pseudomallei diagnostic pattern discovery

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An evaluation of the accuracy of small-area demographic estimates of population at risk and its effect on prevalence statistics

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A graph theoretical approach to data fusion

Žurauskienė, J., Kirk, P., & Stumpf, M. P. H. (2016) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2016-0016 · Cited by 5

The influence of molecular order and microstructure on the R2* and the magnetic susceptibility tensor

Wisnieff, C., Liu, T., Wang, Y., & Spincemaille, P. (2015) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2015.12.003 · Cited by 5

Similarity in functional brain architecture between rest and specific task modes: A model of genetic and environmental contributions to episodic memory

Petrican, R., & Levine, B. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.057 · Cited by 5

Noninvasive Neuromonitoring of Hypothermic Circulatory Arrest in Aortic Surgery

Stewart, J. A., Särkelä, M. O. K., Salmi, T. K., Wennervirta, J. E., Vakkuri, A., Vainikka, T. L. S., Suojaranta, R., Mäki, K., Ilkka, V. H., Viertiö‐Oja, H., et al. (2019) · Scandinavian Journal of Surgery · doi:10.1177/1457496919863942 · Cited by 5

Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

Laureau, A., Lamirand, V. P., Rochman, D. A., & Pautz, A. (2020) · EPJ Nuclear Sciences & Technologies · doi:10.1051/epjn/2020004 · Cited by 5

Biased bootstrap sampling for efficient two-sample testing

Gillam, T., & Lester, C. G. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.34613 · Cited by 5

Empirical Orthogonal Function (EOF) Analysis

Martinson, D. G. (2018) · Cambridge University Press eBooks · doi:10.1017/9781139342568.016 · Cited by 5

Why do services and manufacturing firms envision environmental innovation differently? A path-model comparison

Peiró‐Signes, Á., & Alvarez, C. (2014) · RiuNet (Universitat Politècnica de València) · Cited by 5

On finite mixtures of Discretized Beta model for ordered responses

Simone, R. (2022) · Test · doi:10.1007/s11749-022-00800-7 · Cited by 5

Wind turbine quantification and reduction of uncertainties based on a data-driven data assimilation approach

Hirvoas, A., Prieur, C., Arnaud, É., Caleyron, F., & Zuniga, M. M. (2022) · Journal of Renewable and Sustainable Energy · doi:10.1063/5.0086255 · Cited by 5

Confidence intervals in molecular dating by maximum likelihood

Paradis, E., Claramunt, S., Brown, J. W., & Schliep, K. P. (2022) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2022.107652 · Cited by 5

Simulation study to evaluate when Plasmode simulation is superior to parametric simulation in estimating the mean squared error of the least squares estimator in linear regression

Stolte, M., Schreck, N., Slynko, A., Saadati, M., Benner, A., Rahnenführer, J., & Bommert, A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299989 · Cited by 5

Hyperphenylalaninaemias in Estonia: Genotype–Phenotype Correlation and Comparative Overview of the Patient Cohort Before and After Nation-Wide Neonatal Screening

Lilleväli, H., Reinson, K., Muru, K., Simenson, K., Murumets, Ü., Möls, T., & Õunap, K. (2017) · JIMD Reports · doi:10.1007/8904_2017_61 · Cited by 5

Electroconvulsive therapy “corrects” the neural architecture of visuospatial memory: Implications for typical cognitive-affective functioning

Petrican, R., Söderlund, H., Kumar, N., Daskalakis, Z. J., Flint, A. J., & Levine, B. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101816 · Cited by 5

Functional autoregressive forecasting of long-term seabed evolution

Guillas, S., Bakare, A., Morley, J. G., & Simons, R. R. (2009) · Journal of Coastal Conservation · doi:10.1007/s11852-009-0085-4 · Cited by 5

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression

Le, H., & Marcus, J. (2012) · Educational and Psychological Measurement · doi:10.1177/0013164412445298 · Cited by 5

On the calibration process of film dosimetry: OLS inverse regression versus WLS inverse prediction

Crop, F., Rompaye, B. V., Paelinck, L., Vakaet, L. C., Thierens, H. M., & Wagter, C. D. (2008) · Physics in Medicine and Biology · doi:10.1088/0031-9155/53/14/015 · Cited by 5

Examination of sensitivity of land use parameters and population on the performance of the AERMOD model for an urban area

Bhardwaj, K. S. (2005) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 5

Chapter 9 Growth Characteristics of a Recovering Wolf Population in the Great Lakes Region

Deelen, T. R. V. (2009) · Cited by 5

Uncertainty of trading rules in currency markets: an application of non-parametric bootstrapping

Pasquariello, P. (2002) · Journal of Multinational Financial Management · doi:10.1016/s1042-444x(01)00049-4 · Cited by 5

Estimations of aqueous solubility from N-octanol/water partition coefficients analyzed by the bootstrap method

Halfon, E., Altschuh, J., Brüggemann, R., & Karcher, W. (1991) · Chemosphere · doi:10.1016/0045-6535(91)90253-a · Cited by 5

Improving interpretation by orthogonal variation : Multivariate analysis of spectroscopic data

Stenlund, H. C. (2011) · DiVA at Umeå University (Umeå University) · Cited by 5

Recent Literature on Testing for Intergroup Concordance

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Implementing result-based agri-environmental payments by means of modelling

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High-temperature thermophysical properties of liquid zirconium: Quantum molecular dynamics and pulse heating experiment

Paramonov, M. A., Minakov, D. V., Dorovatovsky, A. V., Sheindlin, M., Fokin, V. B., Demyanov, G. S., & Levashov, P. R. (2024) · Physical review. B./Physical review. B · doi:10.1103/physrevb.110.184204 · Cited by 4

Machine Learning Applied to Electrochemical Data Processing for Improved Analyte Quantification in Complex Saliva

Buddhacharya, S. M., Ramsey, A., Ramsey, S. A., & Fu, E. S. (2025) · Electroanalysis · doi:10.1002/elan.70048 · Cited by 4

Bootstrapping Phylogenies Inferred from Rearrangement Data

Lin, Y., Rajan, V., & Moret, B. M. E. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23038-7_16 · Cited by 4

An examination of sleep as a protective factor for depression and anxiety in the perinatal period: novel causal analyses in a prospective pregnancy cohort

Basu, A., Sarvet, A. L., Chen, J. T., Denckla, C. A., Zhu, Y., & Koenen, K. C. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae349 · Cited by 4

Super-high tumor mutational burden predicts complete remission following immunotherapy: from Peto’s paradox to druggable cancer hallmark

Zheng, M. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-010486 · Cited by 5

Quantifying uncertainty: all we need is the bootstrap?

Zrimšek, U., & Štrumbelj, E. (2025) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2025.2577274 · Cited by 4

Microdemographic Determinants of Population Recovery among the Northern Aché

Baker, Hill, Hurtado, Alcantara, Hunsinger, & Sprague (2015) · Human Biology · doi:10.13110/humanbiology.87.1.0005 · Cited by 4

Privacy-Preserving Methods for Repeated Measures Designs

Liou, K., Zheng, W., & Anand, S. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524216 · Cited by 4

A novel approach combining bootstrapped non-intrusive reduced order models and unscented transform for the robust and efficient CFD analysis of accidental gas releases in congested plants

Abrate, N., Moscatello, A., Ledda, G., Pedroni, N., Carbone, F., Maffia, E., & Carpignano, A. (2023) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2023.105015 · Cited by 4

Teamwork Coaching in the Research Development Process

Stephens, B., Downer, J. B., & Cummings, J. N. (2024) · Small Group Research · doi:10.1177/10464964241240721 · Cited by 4

Standard Errors of Indices in Rough Set Data Analysis

Gediga, G., & Düntsch, I. (2014) · Lecture notes in computer science · doi:10.1007/978-3-642-54756-0_2 · Cited by 4

QUALITY TEST OF INTERPOLATION METHODS ON STEEPNESS REGIONS FOR THE USE IN SURFACE MODELLING

Tunalıoğlu, N. (2012) · Tehnicki vjesnik - Technical Gazette · Cited by 4

Assessment of insurance coverage and claims in rainfall related risks in processing tomato in Western Spain

Castañeda-Vera, A., Barrios, L., Garrido, A., & Mínguez, M. I. (2014) · European Journal of Agronomy · doi:10.1016/j.eja.2014.05.005 · Cited by 4

Interval Estimation Method for Decision Making in Wavelet-Based Software Reliability Assessment

Xiao, X. I. A. O., & Dohi, T. (2014) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e97.d.1058 · Cited by 4

A Study on the Efficiency Analysis of R&D General Management Cost for Domestic R&D Agency Institutes

Song, G., Yoo, H., Kim, K., & Jang, H. (2015) · Journal of the Korean society for quality management · doi:10.7469/jksqm.2015.43.1.085 · Cited by 4

GSD: An SPSS extension command for sub-sampling and bootstrapping datasets

Harding, B., & Cousineau, D. (2016) · The Quantitative Methods for Psychology · doi:10.20982/tqmp.12.2.p138 · Cited by 4

Multi-objective Search for Comprehensible Rule Ensembles

Błaszczyński, J., Prusak, B., & Słowiński, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47160-0_46 · Cited by 4

Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing

Ma, C., Xue, Y., & Ma, S. (2019) · Cancers · doi:10.3390/cancers11111732 · Cited by 4

A Non-Monetary Multidimensional Poverty Analysis of Tunisia Using Generalized Sen-Shorrocks-Thon Measures

Chtioui, N., & Ayadi, M. (2012) · Economic studies in inequality, social exclusion and well-being · doi:10.1007/978-1-4614-5263-8_6 · Cited by 4

Use of Jackknifing to Evaluate Effects of Anchor Item Selection on Equating With the Nonequivalent Groups With Anchor Test (NEAT) Design

Lu, R., Haberman, S. J., Guo, H., & Liu, J. (2015) · ETS Research Report Series · doi:10.1002/ets2.12056 · Cited by 4

Age and growth of Cape stumpnoseRhabdosargus holubi(Pisces: Sparidae) in the Eastern Cape, South Africa

Farthing, M., James, N. C., & Potts, W. M. (2016) · African Journal of Marine Science · doi:10.2989/1814232x.2016.1156577 · Cited by 4

Safe by Design: An Exploration of Jail-Based Injury Across New York City

John, V. S., Blount‐Hill, K., Mufarreh, A., & Lutgen-Nieves, L. (2022) · Journal of Correctional Health Care · doi:10.1089/jchc.20.07.0059 · Cited by 4

Interval estimation for individual categories in cumulative logit models

Rajeswaran, J., & Blackstone, E. H. (2007) · Statistics in Medicine · doi:10.1002/sim.2854 · Cited by 4

Preliminary investigation of a humoral and cell-mediated immunity ratio for diagnosis of paratuberculosis in beef cattle

Fosgate, G. T., Osterstock, J. B., Benjamin, L., Dobek, G. L., & Roussel, A. J. (2009) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2009.06.001 · Cited by 4

Minimizing raw water NOM concentration through optimized source water selection

Weiss, W. J., Schindler, S. C., Freud, S., Herzner, J. A., Hoek, K. F., Wright, B. A., Reckhow, D. A., & Becker, W. C. (2013) · American Water Works Association · doi:10.5942/jawwa.2013.105.0119 · Cited by 4

Resampling-based Classification Using Depth for Functional Curves

Kwon, A. M., Ouyang, M., & Cheng, A. (2014) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2014.944652 · Cited by 4

Age and growth of longnose trevally (Carangoides chrysophrys) in the Arabian Sea

Al-Rasady, I. H., Govender, A., & Jufaili, S. M. A. (2013) · Journal of Applied Ichthyology · doi:10.1111/jai.12222 · Cited by 4

Multivariate explanatory model for sporadic carcinoma of the colon in Dukes' stages I and IIa

Villadiego-Sánchez, J. M., Ortega‐Calvo, M., Pino‐Mejías, R., Cayuela, A., Iglesias-Bonilla, P., la Corte, F. G.-D., Santos‐Lozano, J. M., & Lapetra-Peralta, J. (2009) · International Journal of Medical Sciences · doi:10.7150/ijms.6.43 · Cited by 4

Inference on Clustered Survival Data Using Imputed Frailties

Li, Y., RYAN, L. M., Bellamy, S. L., & Satten, G. A. (2003) · Journal of Computational and Graphical Statistics · doi:10.1198/1061860032247 · Cited by 4

Empirical Information Metrics for Prediction Power and Experiment Planning

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Coexistence calculation using the isothermal-isochoric integration method

Razavi, S. M., Messerly, R. A., & Elliott, J. R. (2019) · Fluid Phase Equilibria · doi:10.1016/j.fluid.2019.06.026 · Cited by 4

Bootstrapping und andere Resampling-Methoden

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Multivariate analysis in the computer age

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The bootstrap and the jackknife in ecotoxicology or nonparametric estimates of standard error

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Evaluation of a neonatal prediction function for cardiovascular risk assessment

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Interval estimation of population parameters based on environmental data with detection limits

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On the Trend of Gini Coefficient in the Greek Economic Environment During the Years 1962 to 2002Preprint

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One‐ and Two‐Sample Tests of Kendall's τ

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Bootstrapping the correlation coefficient: a comparison of smoothing strategies

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The use of glim and the bootstrap in assessing a clinical trial of two drugs

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Inference on a distribution from noisy draws

Jochmans, K., & Weidner, M. (2019) · doi:10.1920/wp.cem.2019.4419 · Cited by 4

Evolution of life-history timing in a leafmining moth: Phenotypic selection in patches with manipulated development time

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NONPARAMETRIC FRAILTY MODELS FOR CLUSTERED SURVIVAL DATA

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Cytokine-expression patterns reveal coordinated immunological programs associated with persistent MRSA bacteremiaPreprint

Chin, J. L., Tan, Z. C., Chan, L. C., Ruffin, F., Parmar, R., Ahn, R. S., Taylor, S. D., Bayer, A. S., Hoffmann, A., Fowler, V. G., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.28.521386 · Cited by 4

Partial least squares structural equation modelling with incomplete data : an investigation of the impact of imputation methods

Jamil, M. F. A. (2012) · Bradford Scholars (University of Bradford) · Cited by 4

Beyond proximity: an observational study of stillbirth rates and emergency obstetric and newborn care accessibility in The Gambia

Wariri, O., Dotse‐Gborgbortsi, W., Agbla, S. C., Jah, H., Cham, M., Jawara, B. F., Bittaye, M., Nyassi, M. T., Marena, M., Sanneh, S., et al. (2025) · BMJ Global Health · doi:10.1136/bmjgh-2024-016579 · Cited by 3

Efficacy of an HIV Prevention Intervention for African American Adolescent Girls: A Randomized Controlled Trial

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Nonparametric standard errors and confidence intervals

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The Bootstrap and the Jackknife

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A leisurely look at the bootstrap, the jackknife

Efron, B., & Gong, G. D. (1983)

The jackknife, thebootstrap and othernonparametric methods

Φράγκος, Χ. Κ. (2015)

An Introduction to the Bootstrap

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