Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression

Autores:: Cruz, Laura
Blanco, Jessica
Giraldo, Ramón

Tipo de recurso:: Article of journal

Fecha de publicación:: 2024

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

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dc.title.es-ES.fl_str_mv	Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression
title	Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression
spellingShingle	Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression Bootstrap, estimación kernel de la densidad, intervalos de confianza, Jackknife, potencia de la prueba, regresión kernel Bootstrap, confidence intervals, Jackknife, kernel regression, local regression, power of the test
title_short	Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression
title_full	Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression
title_fullStr	Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression
title_full_unstemmed	Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression
title_sort	Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression
dc.creator.fl_str_mv	Cruz, Laura Blanco, Jessica Giraldo, Ramón
dc.contributor.author.spa.fl_str_mv	Cruz, Laura Blanco, Jessica Giraldo, Ramón
dc.subject.none.fl_str_mv	Bootstrap, estimación kernel de la densidad, intervalos de confianza, Jackknife, potencia de la prueba, regresión kernel Bootstrap, confidence intervals, Jackknife, kernel regression, local regression, power of the test
topic	Bootstrap, estimación kernel de la densidad, intervalos de confianza, Jackknife, potencia de la prueba, regresión kernel Bootstrap, confidence intervals, Jackknife, kernel regression, local regression, power of the test
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-12-11 00:00:00 2026-04-16T17:12:28Z
dc.date.available.none.fl_str_mv	2024-12-11 00:00:00
dc.date.issued.none.fl_str_mv	2024-12-11
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.doi.none.fl_str_mv	10.19053/01217488.v15.n2.2024.15900
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dc.relation.references.none.fl_str_mv	Behme, A. Theory of Stochastic Objects: Probability, Stochastic Processes and Inference. Chapman & Hall/CRC Press, 2018 Bonamente, M. Monte Carlo and Re-sampling Methods. In Statistics and Analysis of Scientific Data (pp. 351-367). Singapore: Springer Nature Singapore, 2022. Bose, A., Chatterjee, S. Comparison of bootstrap and jackknife variance estimators in linear regression: Second order results. Statistica Sinica. 2002, 575-598. Bowman, A., Azzalini, A. Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations. OUP Oxford, 1973. Buckland, S. Monte Carlo confidence intervals. Biometrics. 1984; 811-817. Chen, Y. A tutorial on kernel density estimation and recent advances. Biostatistics & Epidemiology. 2017; 1(1), 161-187. DiCiccio, T., Efron, B. Bootstrap confidence intervals. Statistical Science. 1996; 11(3): 189-228. Donegani, M. Bootstrap adaptive test for two way analysis of variance. Biometrical Journal. 1992; 34(2): 141-146 Donnelly-Makowecki, L., Moore, R. Hierarchical testing of three rainfall-runoff models in small forested catchments. Journal of Hydrology. 1999; 219(3-4): 136-152. Efron, B., Stein, C. The Jackknife estimate of variance. The Annals of Statistics. 1981; 586-596. Efron B., Tibshirani R. An introduction to the bootstrap. 1994; CRC Press. Freedman D., Peters S. Bootstrapping a regression equation: Some empirical results. Journal of Statistical Software. 2008; 25(7):1-37. Garay, A., Hashimoto, E., Ortega, E., Lachos, V. On estimation and influence diagnostics for zeroinflated negative binomial regression models. Computational Statistics & Data Analysis, 2011; 55(3): 1304-1318. Gillibrand E., Jamieson A., Bagley, P., Zuur, A., Priede, I. Seasonal development of a deep pelagic bioluminescent layer in the temperate NE Atlantic Ocean. Marine Ecology Progress Series, 2007, 341: 37-43. Gong, G. Cross-validation, the jackknife, and the bootstrap: Excess error estimation in forward logistic regression. Journal of the American Statistical Association. 1986; 81 (393): 108-113. Hahn, J. Bootstrapping quantile regression estimators. Econometric Theory. 1995; 11(1): 105-121. Han, J., Li, G., Liu, H., Hu, H., Zhang, X. Stimulation of bioluminescence in Noctiluca sp. using controlled temperature changes. Luminescence. 2013; 28(5): 742-744. Heger, A., Ieno, E., King, N., Morris, K., Bagley, P., Priede, I. Deep-sea pelagic bioluminescence over the Mid-Atlantic Ridge. Deep Sea Research Part II: Topical Studies in Oceanography. 2008, 55(1-2), 126-136. Kohler, M., Schindler, A., Sperlich, S. 2014. A review and comparison of bandwidth selection methods for kernel regression. International Statistical Review. 2014; 82(2): 243-274. Lu, X., Su, L. Jackknife model averaging for quantile regressions. Journal of Econometrics. 2015; 188(1): 40-58. McCabe, P., Korb, O., Cole, J. Kernel density estimation applied to bond length, bond angle, and torsion angle distributions. Journal of Chemical Information and Modeling, 2014, 54(5): 1284-1288. Moulton, L., Zeger, S. Bootstrapping generalized linear models. Computational Statistics & Data Analysis. 1991; 11: 53-63. OÂ´Brien, S., Webb, A., Brewer, M., Reid, J. Use of kernel density estimation and maximum curvature to set marine protected area boundaries: Identifying a special protection area for wintering red-throated divers in the UK. Biological Conservation. 2012, 156: 15-21. Oyeyemi, G. Comparison of bootstrap and jackknife methods of resampling in estimating popultion parameters. Global Journal of Pure and Applied Sciences. 2008; 1(2):217-220. Quenouille, M. Approximate test of correlation in time series. Journal of the Royal Statistical Society, Series B. 1949; 11:68-84 Quenouille, M. Notes on bias in estimation. Biometrika. 1956; 43(3): 353-360. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/. 2022. Roda, A., Pasini, P., Mirasoli, M., Michelini, E., Guardigli, M. Biotechnological applications of bioluminescence and chemiluminescence. TRENDS in Biotechnology. 2004, 22(6), 295-303. Rudemo, M. Empirical choice of histograms and kernel density estimators. Scandinavian Journal of Statistics. 1982, 65-78. Scott, D. Histogram. Wiley Interdisciplinary Reviews: Computational Statistics. 2010, 2(1): 44-48. Sherman, M., Cessie, S. A comparison between bootstrap methods and generalized estimating equations for correlated outcomes in generalized linear models. Communications in Statistics-Simulation and Computation. 1997; 26(3): 901-925. Tukey, J. Bias and confidence in not quite large samples. Ann. Math. Statist. 1958; 29: 614. Widder, E. Marine bioluminescence. Why do so many animals in the open ocean make light?. Science. 2010; 328: 704-708. Wu, C. Jackknife, Bootstrap and other resampling methods in regression analysis. The Annals of Statistics. 1986; 14(4): 1261-1295. Zambom, A., Dias, R. A review of kernel density estimation with applications to econometrics. International Econometric Review. 2013; 5(1): 20-42. Zuur, A., Ieno, E. Smith, G. Analysing ecological data. Springer; 2007. Zuur, A. Additive mixed modelling applied on deep-sea pelagic bioluminescent organisms. In Mixed effects models and extensions in ecology with R. 2009; 399-422.
dc.relation.citationendpage.none.fl_str_mv	152
dc.relation.citationissue.spa.fl_str_mv	2
dc.relation.citationstartpage.none.fl_str_mv	143
dc.relation.citationvolume.spa.fl_str_mv	15
dc.relation.ispartofjournal.spa.fl_str_mv	Ciencia en Desarrollo
dc.relation.bitstream.none.fl_str_mv	https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/download/15900/14902
dc.relation.citationedition.spa.fl_str_mv	Núm. 2 , Año 2024 : Vol. 15 Núm. 2 (2024): Vol 15, Núm.2 (2024): Julio-Diciembre
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dc.publisher.spa.fl_str_mv	Universidad Pedagógica y Tecnológica de Colombia
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spelling	Cruz, LauraBlanco, JessicaGiraldo, Ramón2024-12-11 00:00:002026-04-16T17:12:28Z2024-12-11 00:00:002024-12-110121-7488https://repositorio.uptc.edu.co/handle/001/1873310.19053/01217488.v15.n2.2024.159002462-7658https://doi.org/10.19053/01217488.v15.n2.2024.15900application/pdfUniversidad Pedagógica y Tecnológica de Colombiahttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esinfo:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/15900Bootstrap, estimación kernel de la densidad, intervalos de confianza, Jackknife, potencia de la prueba, regresión kernelBootstrap, confidence intervals, Jackknife, kernel regression, local regression, power of the testBootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regressionArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85Behme, A. Theory of Stochastic Objects: Probability, Stochastic Processes and Inference. Chapman & Hall/CRC Press, 2018Bonamente, M. Monte Carlo and Re-sampling Methods. In Statistics and Analysis of Scientific Data (pp. 351-367). Singapore: Springer Nature Singapore, 2022.Bose, A., Chatterjee, S. Comparison of bootstrap and jackknife variance estimators in linear regression: Second order results. Statistica Sinica. 2002, 575-598.Bowman, A., Azzalini, A. Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations. OUP Oxford, 1973.Buckland, S. Monte Carlo confidence intervals. Biometrics. 1984; 811-817.Chen, Y. A tutorial on kernel density estimation and recent advances. Biostatistics & Epidemiology. 2017; 1(1), 161-187.DiCiccio, T., Efron, B. Bootstrap confidence intervals. Statistical Science. 1996; 11(3): 189-228.Donegani, M. Bootstrap adaptive test for two way analysis of variance. Biometrical Journal. 1992; 34(2): 141-146Donnelly-Makowecki, L., Moore, R. Hierarchical testing of three rainfall-runoff models in small forested catchments. Journal of Hydrology. 1999; 219(3-4): 136-152.Efron, B., Stein, C. The Jackknife estimate of variance. The Annals of Statistics. 1981; 586-596.Efron B., Tibshirani R. An introduction to the bootstrap. 1994; CRC Press.Freedman D., Peters S. Bootstrapping a regression equation: Some empirical results. Journal of Statistical Software. 2008; 25(7):1-37.Garay, A., Hashimoto, E., Ortega, E., Lachos, V. On estimation and influence diagnostics for zeroinflated negative binomial regression models. Computational Statistics & Data Analysis, 2011; 55(3): 1304-1318.Gillibrand E., Jamieson A., Bagley, P., Zuur, A., Priede, I. Seasonal development of a deep pelagic bioluminescent layer in the temperate NE Atlantic Ocean. Marine Ecology Progress Series, 2007, 341: 37-43.Gong, G. Cross-validation, the jackknife, and the bootstrap: Excess error estimation in forward logistic regression. Journal of the American Statistical Association. 1986; 81 (393): 108-113.Hahn, J. Bootstrapping quantile regression estimators. Econometric Theory. 1995; 11(1): 105-121.Han, J., Li, G., Liu, H., Hu, H., Zhang, X. Stimulation of bioluminescence in Noctiluca sp. using controlled temperature changes. Luminescence. 2013; 28(5): 742-744.Heger, A., Ieno, E., King, N., Morris, K., Bagley, P., Priede, I. Deep-sea pelagic bioluminescence over the Mid-Atlantic Ridge. Deep Sea Research Part II: Topical Studies in Oceanography. 2008, 55(1-2), 126-136.Kohler, M., Schindler, A., Sperlich, S. 2014. A review and comparison of bandwidth selection methods for kernel regression. International Statistical Review. 2014; 82(2): 243-274.Lu, X., Su, L. Jackknife model averaging for quantile regressions. Journal of Econometrics. 2015; 188(1): 40-58.McCabe, P., Korb, O., Cole, J. Kernel density estimation applied to bond length, bond angle, and torsion angle distributions. Journal of Chemical Information and Modeling, 2014, 54(5): 1284-1288.Moulton, L., Zeger, S. Bootstrapping generalized linear models. Computational Statistics & Data Analysis. 1991; 11: 53-63.OÂ´Brien, S., Webb, A., Brewer, M., Reid, J. Use of kernel density estimation and maximum curvature to set marine protected area boundaries: Identifying a special protection area for wintering red-throated divers in the UK. Biological Conservation. 2012, 156: 15-21.Oyeyemi, G. Comparison of bootstrap and jackknife methods of resampling in estimating popultion parameters. Global Journal of Pure and Applied Sciences. 2008; 1(2):217-220.Quenouille, M. Approximate test of correlation in time series. Journal of the Royal Statistical Society, Series B. 1949; 11:68-84Quenouille, M. Notes on bias in estimation. Biometrika. 1956; 43(3): 353-360.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/. 2022.Roda, A., Pasini, P., Mirasoli, M., Michelini, E., Guardigli, M. Biotechnological applications of bioluminescence and chemiluminescence. TRENDS in Biotechnology. 2004, 22(6), 295-303.Rudemo, M. Empirical choice of histograms and kernel density estimators. Scandinavian Journal of Statistics. 1982, 65-78.Scott, D. Histogram. Wiley Interdisciplinary Reviews: Computational Statistics. 2010, 2(1): 44-48.Sherman, M., Cessie, S. A comparison between bootstrap methods and generalized estimating equations for correlated outcomes in generalized linear models. Communications in Statistics-Simulation and Computation. 1997; 26(3): 901-925.Tukey, J. Bias and confidence in not quite large samples. Ann. Math. Statist. 1958; 29: 614.Widder, E. Marine bioluminescence. Why do so many animals in the open ocean make light?. Science. 2010; 328: 704-708.Wu, C. Jackknife, Bootstrap and other resampling methods in regression analysis. The Annals of Statistics. 1986; 14(4): 1261-1295.Zambom, A., Dias, R. A review of kernel density estimation with applications to econometrics. International Econometric Review. 2013; 5(1): 20-42.Zuur, A., Ieno, E. Smith, G. Analysing ecological data. Springer; 2007.Zuur, A. Additive mixed modelling applied on deep-sea pelagic bioluminescent organisms. In Mixed effects models and extensions in ecology with R. 2009; 399-422.152214315Ciencia en Desarrollohttps://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/download/15900/14902Núm. 2 , Año 2024 : Vol. 15 Núm. 2 (2024): Vol 15, Núm.2 (2024): Julio-DiciembrePublicationOREORE.xmltext/xml2733https://repositorio.uptc.edu.co/bitstreams/59d17c21-c3cf-46ae-980e-afcf57175994/download18cf220550167a83b9a98765da9819adMD51falseAnonymousREAD001/18733oai:repositorio.uptc.edu.co:001/187332026-10-30 07:01:47.98https://creativecommons.org/licenses/by-nc-nd/4.0/deed.esmetadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Bootstrap versus Jackknife: Confidence intervals, hypothesis testing, density estimation, and kernel regression

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