Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods
Abstract
We discuss several nonparametric methods for attaching a standard error to a point estimate: the jackknife, the bootstrap, half-sampling, subsampling, balanced repeated replications, the infinitesimal jackknife, influence function techniques and the delta method. The last three methods are shown to be identical. All the methods derive from the same basic idea, which is also the idea underlying the common parametric methods. Extended numerical comparisons are made for the special case of the correlation coefficient.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some data within this study were not normally distributed; non-parametric tests and bootstrap were used to obtain robust results.
“First, some data within this study was not normally distributed. Typically used parametric methods would have led to biased results. Non-parametric tests and bootstrap were used in the analyses to obtain robust results [53–55].”
From Impact of an acceptance facilitating intervention on psychotherapists' acceptance of blended therapy (2020) · cites this paper as [53]
Cites
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602 · see citing works
Self-citationContinuous Univariate Distributions.
Omori, Y., Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995) · Journal of the American Statistical Association · doi:10.2307/2291547 · Cited by 9,085 · see citing works
The Influence Curve and its Role in Robust Estimation
Hampel, F. R. (1974) · Journal of the American Statistical Association · doi:10.1080/01621459.1974.10482962 · Cited by 2,554 · see citing works
The Jackknife Estimate of Variance
Efron, B., & Stein, C. (1981) · The Annals of Statistics · doi:10.1214/aos/1176345462 · Cited by 1,424 · see citing works
Self-citationMiller, R. G. (1974) · Biometrika · doi:10.1093/biomet/61.1.1 · Cited by 1,391 · see citing works
Computers and the Theory of Statistics: Thinking the Unthinkable
Efron, B. (1979) · SIAM Review · doi:10.1137/1021092 · Cited by 880 · see citing works
Self-citationCensored Data and the Bootstrap
Efron, B. (1981) · Journal of the American Statistical Association · doi:10.1080/01621459.1981.10477650 · Cited by 689 · see citing works
Self-citationInference from Complex Samples
Kish, L., & Frankel, M. R. (1974) · Journal of the Royal Statistical Society Series B (Methodological) · doi:10.1111/j.2517-6161.1974.tb00981.x · Cited by 530 · see citing works
Robust estimation and outlier detection with correlation coefficients
Devlin, S. J., Gnanadesikan, R., & Kettenring, J. R. (1975) · Biometrika · doi:10.1093/biomet/62.3.531 · Cited by 385 · see citing works
Pseudo-Replication: Half Samples
McCarthy, P. J. (1969) · Revue de l Institut International de Statistique / Review of the International Statistical Institute · doi:10.2307/1402116 · Cited by 177 · see citing works
Using Subsample Values as Typical Values
Hartigan, J. A. (1969) · Journal of the American Statistical Association · doi:10.1080/01621459.1969.10501057 · Cited by 130 · see citing works
Improving the jackknife with special reference to correlation estimation
HINKLEY, D. V. (1978) · Biometrika · doi:10.1093/biomet/65.1.13 · Cited by 95 · see citing works
A note on exact robust confidence intervals for location
Maritz, J. S. (1979) · Biometrika · doi:10.1093/biomet/66.1.163 · Cited by 69 · see citing works
Error Analysis by Replaced Samples
Hartigan, J. A. (1971) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1971.tb00861.x · Cited by 22 · see citing works
Efficiency of confidence intervals generated by repeated subsample calculations
Forsythe, A. B., & HARTICAN, J. A. (1970) · Biometrika · doi:10.1093/biomet/57.3.629 · Cited by 18 · see citing works
On the generalized jackknife and its relation to statistical differentials
Gray, H. L., Schucany, W. R., & Watkins, T. A. (1975) · Biometrika · doi:10.1093/biomet/62.3.637 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088 · see citing works
Efron, B., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013815 · Cited by 6,033 · see citing works
Self-citationEvaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation
Legates, D. R., & McCabe, G. J. (1999) · Water Resources Research · doi:10.1029/1998wr900018 · Cited by 4,608 · see citing works
A leisurely look at the bootstrap, the jackknife, and cross-validation
Efron, B., & Gong, G. (1983) · The American Statistician · doi:10.1080/00031305.1983.10483087 · Cited by 3,229 · see citing works
Self-citationBootstrap confidence intervals
DiCiccio, T. J., & Efron, B. (1996) · Statistical Science · doi:10.1214/ss/1032280214 · Cited by 2,286 · see citing works
Self-citationStatistics for the evaluation and comparison of models
Willmott, C. J., Ackleson, S. G., Davis, R. E. L., Feddema, J. J., Klink, K., Legates, D. R., O’Donnell, J., & Rowe, C. M. (1985) · Journal of Geophysical Research Atmospheres · doi:10.1029/jc090ic05p08995 · Cited by 2,066 · see citing works
Advantages of Monte Carlo confidence intervals for indirect effects
Preacher, K. J., & Selig, J. P. (2012) · Communication Methods and Measures · doi:10.1080/19312458.2012.679848 · Cited by 1,908 · see citing works
Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0300-3 · Cited by 1,812 · see citing works
1 Economic Geography and International Inequality
Globalisation Programme, Redding, S. J., & Venables, A. J. (2010) · Cited by 1,575 · see citing works
Meade, M. O., Cook, D. J., Guyatt, G. H., Slutsky, A. S., Arabi, Y. M., Cooper, D. J., Davies, A. R., Hand, L. E., Zhou, Q., Thabane, L., et al. (2008) · JAMA · doi:10.1001/jama.299.6.637 · Cited by 1,370 · see citing works
Testing for ancient admixture between closely related populations
Durand, E. Y., Patterson, N., Reich, D., & Slatkin, M. (2011) · Molecular Biology and Evolution · doi:10.1093/molbev/msr048 · Cited by 1,375 · see citing works
Fernandes, A. D., Reid, J. N., Macklaim, J. M., McMurrough, T. A., Edgell, D. R., & Gloor, G. B. (2014) · Microbiome · doi:10.1186/2049-2618-2-15 · Cited by 1,329 · see citing works
A NEW ELECTRON-DENSITY MODEL FOR ESTIMATION OF PULSAR AND FRB DISTANCES
Yao, J. M., Manchester, R. N., & Wang, N. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/835/1/29 · Cited by 1,194 · see citing works
A Primer on Maximum Likelihood Algorithms Available for Use With Missing Data
Enders, C. K. (2001) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0801_7 · Cited by 1,027 · see citing works
Ritter, A., & Muñoz‐Carpena, R. (2012) · Journal of Hydrology · doi:10.1016/j.jhydrol.2012.12.004 · Cited by 996 · see citing works
Balogh, M. L., Morris, S. L., Yee, H. K. C., Carlberg, R. G., & Ellingson, E. (1999) · The Astrophysical Journal · doi:10.1086/308056 · Cited by 982 · see citing works
Ten quick tips for machine learning in computational biology
Chicco, D. (2017) · BioData Mining · doi:10.1186/s13040-017-0155-3 · Cited by 875 · see citing works
Murphy, K. R., Butler, K. D., Spencer, R. G. M., Stedmon, C. A., Boehme, J. R., & Aiken, G. R. (2010) · Environmental Science & Technology · doi:10.1021/es102362t · Cited by 758 · see citing works
Nonparametric standard errors and confidence intervals
Efron, B. (1981) · Canadian Journal of Statistics · doi:10.2307/3314608 · Cited by 683 · see citing works
Self-citationGould, M. K., Kuschner, W. G., Rydzak, C. E., Maclean, C. C., Demas, A. N., Shigemitsu, H., Chan, J. K., & Owens, D. K. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-139-11-200311180-00013 · Cited by 682 · see citing works
Lee, D. H., Buth, K. J., Martin, B., Yip, A. M., & Hirsch, G. M. (2010) · Circulation · doi:10.1161/circulationaha.108.841437 · Cited by 677 · see citing works
Statistical mechanics of networks
Park, J. M., & Newman, M. E. J. (2004) · Physical Review E · doi:10.1103/physreve.70.066117 · Cited by 684 · see citing works
Hawkins, J. D., Graham, J. A., Maguin, E., Abbott, R., Hill, K. G., & Catalano, R. F. (1997) · Journal of Studies on Alcohol · doi:10.15288/jsa.1997.58.280 · Cited by 662 · see citing works
A maximum pseudo-likelihood approach for estimating species trees under the coalescent model
Liu, L., Yu, L., & Edwards, S. V. (2010) · BMC Evolutionary Biology · doi:10.1186/1471-2148-10-302 · Cited by 607 · see citing works
Naturalistic Stimuli in Neuroscience: Critically Acclaimed
Sonkusare, S., Breakspear, M. J., & Guo, C. C. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.05.004 · Cited by 607 · see citing works
A global nucleon optical model potential
Varner, R. L., Thompson, W. J., McAbee, T. L., Ludwig, E. J., & Clegg, T. B. (1991) · Physics Reports · doi:10.1016/0370-1573(91)90039-o · Cited by 564 · see citing works
Differential Galaxy Evolution in Cluster and Field Galaxies at z=0.3Preprint
Balogh, M. L., Morris, S. L., Yee, H. K. C., Carlberg, R. G., & Ellingson, E. (1999) · arXiv (Cornell University) · doi:10.48550/arxiv.astro-ph/9906470 · Cited by 520 · see citing works
A Re-Analysis of the Cochrane Library Data: The Dangers of Unobserved Heterogeneity in Meta-Analyses
Kontopantelis, E., Springate, D. A., & Reeves, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069930 · Cited by 508 · see citing works
Dawson, C. W., Abrahart, R. J., & See, L. (2006) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2006.06.008 · Cited by 504 · see citing works
Oceanic Uptake of Fossil Fuel CO 2 : Carbon-13 Evidence
Quay, P. D., Tilbrook, B., & Wong, C. S. (1992) · Science · doi:10.1126/science.256.5053.74 · Cited by 487 · see citing works
On assuring valid measures for theoretical models using survey data
Ping, R. A. (2003) · Journal of Business Research · doi:10.1016/s0148-2963(01)00297-1 · Cited by 489 · see citing works
Inferential Network Analysis with Exponential Random Graph Models
Cranmer, S., & Desmarais, B. A. (2010) · Political Analysis · doi:10.1093/pan/mpq037 · Cited by 478 · see citing works
Missing Data, Imputation, and the Bootstrap
Efron, B. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476768 · Cited by 471 · see citing works
Self-citationSize Hierarchies in Experimental Populations of Annual Plants
Weiner, J. (1985) · Ecology · doi:10.2307/1940535 · Cited by 471 · see citing works
Estimating Species Phylogenies Using Coalescence Times among Sequences
Liu, L., Yu, L., Pearl, D. K., & Edwards, S. V. (2009) · Systematic Biology · doi:10.1093/sysbio/syp031 · Cited by 446 · see citing works
Fingerprinting food: current technologies for the detection of food adulteration and contamination
Ellis, D. I., Brewster, V. L., Dunn, W. B., Allwood, J. W., Golovanov, A. P., & Goodacre, R. (2012) · Chemical Society Reviews · doi:10.1039/c2cs35138b · Cited by 441 · see citing works
Kiesel, A., Miller, J. O., Jolicœur, P., & Brisson, B. (2007) · Psychophysiology · doi:10.1111/j.1469-8986.2007.00618.x · Cited by 427 · see citing works
Cluster-robust inference: A guide to empirical practice
MacKinnon, J. G., Nielsen, M. Ø., & Webb, M. D. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.04.001 · Cited by 407 · see citing works
The Same Bond at Different Prices: Identifying Search Frictions and Selling Pressures
Feldhütter, P. (2010) · Review of Financial Studies · doi:10.1093/rfs/hhr093 · Cited by 406 · see citing works
Burri, R., Nater, A., Kawakami, T., Mugal, C. F., Olason, P. I., Smeds, L., Suh, A., Dutoit, L., Bureš, S., Garamszegi, L. Z., et al. (2015) · Genome Research · doi:10.1101/gr.196485.115 · Cited by 398 · see citing works
An Inverse Gaussian Process Model for Degradation Data
Wang, X., & Xu, D. (2010) · Technometrics · doi:10.1198/tech.2009.08197 · Cited by 396 · see citing works
The Discounting of Discounts and Promotion Thresholds
Гупта, С., & Cooper, L. G. (1992) · Journal of Consumer Research · doi:10.1086/209310 · Cited by 360 · see citing works
Lachance, J., Vernot, B., Elbers, C. C., Ferwerda, B., Froment, A., Bodo, J.-M., Lema, G., Fu, W., Nyambo, T., Rebbeck, T. R., et al. (2012) · Cell · doi:10.1016/j.cell.2012.07.009 · Cited by 334 · see citing works
Chiari, Y., Cahais, V., Galtier, N., & Delsuc, F. (2012) · BMC Biology · doi:10.1186/1741-7007-10-65 · Cited by 340 · see citing works
Microbiome profiling by Illumina sequencing of combinatorial sequence-tagged PCR products
Gloor, G. B., Hummelen, R., Macklaim, J. M., Dickson, R. J., Fernandes, A. D., MacPhee, R., & Reid, G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015406 · Cited by 330 · see citing works
The Cerebral Metabolic Landscape in Autism: Intercorrelations of Regional Glucose Utilization
Horwitz, B. (1988) · Archives of Neurology · doi:10.1001/archneur.1988.00520310055018 · Cited by 331 · see citing works
A review of very short-term wind and solar power forecasting
Tawn, R., & Browell, J. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111758 · Cited by 319 · see citing works
Beta-Weibull Distribution: Some Properties and Applications to Censored Data
Lee, C., Famoye, F., & Olumolade, O. (2007) · Journal of Modern Applied Statistical Methods · doi:10.22237/jmasm/1177992960 · Cited by 296 · see citing works
Li, H., Mason, D. E., Bieler, T. R., Boehlert, C. J., & Crimp, M. A. (2013) · Acta Materialia · doi:10.1016/j.actamat.2013.08.042 · Cited by 294 · see citing works
Geographically weighted summary statistics — a framework for localised exploratory data analysis
Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (2002) · Computers Environment and Urban Systems · doi:10.1016/s0198-9715(01)00009-6 · Cited by 283 · see citing works
Voice, P. J., Kowalewski, M., & Eriksson, K. A. (2011) · The Journal of Geology · doi:10.1086/658295 · Cited by 281 · see citing works
Analysis with missing data in prevention research.
Graham, J. A., Hofer, S. M., Donaldson, S. I., MacKinnon, D. P., & Schafer, J. L. (1997) · American Psychological Association eBooks · doi:10.1037/10222-010 · Cited by 280 · see citing works
Strub, P. T., Allen, J. S., Huyer, A., Smith, R. L., & Beardsley, R. C. (1987) · Journal of Geophysical Research Atmospheres · doi:10.1029/jc092ic02p01507 · Cited by 274 · see citing works
The genetics of phenotypic plasticity I. Heritability
Scheiner, S. M., & Lyman, R. F. (1989) · Journal of Evolutionary Biology · doi:10.1046/j.1420-9101.1989.2020095.x · Cited by 269 · see citing works
Business Networks, Corporate Governance, and Contracting in the Mutual Fund Industry
Kuhnen, C. M. (2009) · The Journal of Finance · doi:10.1111/j.1540-6261.2009.01498.x · Cited by 251 · see citing works
Salazar, L. F., Vivolo-Kantor, A. M., Hardin, J. W., & Berkowitz, A. D. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3426 · Cited by 256 · see citing works
Patton, D. R., Carlberg, R. G., Marzke, R. O., Pritchet, C. J., da Costa, L. N., & Pellegrini, P. S. (2000) · The Astrophysical Journal · doi:10.1086/308907 · Cited by 249 · see citing works
The influence of involvement on purchase intention for new world wine
Hollebeek, L. D., Jaeger, S. R., Brodie, R. J., & Balemi, A. (2007) · Food Quality and Preference · doi:10.1016/j.foodqual.2007.04.007 · Cited by 245 · see citing works
Linking data to models: data regression
Jaqaman, K., & Danuser, G. (2006) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2030 · Cited by 243 · see citing works
A Review of Estimating Animal Abundance II
Seber, G. A. F. (1992) · International Statistical Review · doi:10.2307/1403646 · Cited by 243 · see citing works
First in, First out? The Effects of Network Externalities on Pioneer Survival
Srinivasan, R., Lilien, G. L., & Rangaswamy, A. (2003) · Journal of Marketing · doi:10.1509/jmkg.68.1.41.24026 · Cited by 236 · see citing works
Model Uncertainty and Robustness
Young, C., & Holsteen, K. (2017) · Sociological Methods & Research · doi:10.1177/0049124115610347 · Cited by 199 · see citing works
The impact of IFRS adoption on foreign direct investment
Gordon, L. A., Loeb, M. P., & Zhu, W. (2012) · Journal of Accounting and Public Policy · doi:10.1016/j.jaccpubpol.2012.06.001 · Cited by 241 · see citing works
The Bootstrap Method for Assessing Statistical Accuracy
Efron, B., & Tibshirani, R. J. (1985) · Behaviormetrika · doi:10.2333/bhmk.12.17_1 · Cited by 238 · see citing works
Self-citationStructure of the 30S translation initiation complex
Simonetti, A., Marzi, S., Myasnikov, A. G., Fabbretti, A., Yusupov, M., Gualerzi, C. O., & Klaholz, B. P. (2008) · Nature · doi:10.1038/nature07192 · Cited by 235 · see citing works
Age differences in the frontoparietal cognitive control network: Implications for distractibility
Campbell, K. L., Grady, C. L., Ng, C., & Hasher, L. (2012) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2012.05.025 · Cited by 232 · see citing works
Statistical testing in electrophysiological studies
Maris, E. (2011) · Psychophysiology · doi:10.1111/j.1469-8986.2011.01320.x · Cited by 233 · see citing works
BIAS, PRECISION, AND ACCURACY OF FOUR MEASURES OF SPECIES RICHNESS
Hellmann, J. J., & Fowler, G. W. (1999) · Ecological Applications · doi:10.1890/1051-0761(1999)009[0824:bpaaof]2.0.co;2 · Cited by 231 · see citing works
Carbon isotopic composition of atmospheric CH4: Fossil and biomass burning source strengths
Quay, P. D., King, S. L., Stutsman, J., Wilbur, D., Steele, L., FUNG, I. Y., Gammon, R. H., Brown, T. A., Farwell, G. W., Grootes, P. M., et al. (1991) · Global Biogeochemical Cycles · doi:10.1029/91gb00003 · Cited by 227 · see citing works
Hall, P. (1985) · Stochastic Processes and their Applications · doi:10.1016/0304-4149(85)90212-1 · Cited by 225 · see citing works
Jackknife-After-Bootstrap Standard Errors and Influence Functions
Efron, B. (1992) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1992.tb01866.x · Cited by 223 · see citing works
Self-citationWork-Related Migration and Poverty Reduction in Nepal
Lokshin, M. M., Bonch-Osmolovskiy, M., & Glinskaya, E. E. (2010) · Review of Development Economics · doi:10.1111/j.1467-9361.2010.00555.x · Cited by 232 · see citing works
Chen, Q., Wynne, R. J., Goulding, P., & Sandoz, D. J. (2000) · Control Engineering Practice · doi:10.1016/s0967-0661(99)00191-4 · Cited by 218 · see citing works
Wingood, G. M., DiClemente, R. J., Mikhail, I. S., Lang, D. L., McCree, D. H., Davies, S. L., Hardin, J. W., Hook, E. W., & Saag, M. S. (2004) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/01.qai.0000140603.57478.a9 · Cited by 218 · see citing works
Nielsen, E., Gamble, J. K., Rudinger, K., Scholten, T. L., Young, K., & Blume-Kohout, R. (2021) · Quantum · doi:10.22331/q-2021-10-05-557 · Cited by 212 · see citing works
On Bootstrap Resampling and Iteration
HALL, P., & Martin, M. A. (1988) · Biometrika · doi:10.1093/biomet/75.4.661 · Cited by 210 · see citing works
The Emergence of Dominant Designs
Srinivasan, R., Lilien, G. L., & Rangaswamt, A. (2006) · Journal of Marketing · doi:10.1509/jmkg.70.2.001 · Cited by 208 · see citing works
Duncan, K., Conselice, C. J., Mundy, C., Bell, E., Donley, J., Galametz, A., Guo, Y., Grogin, N. A., Hathi, N. P., Kartaltepe, J. S., et al. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab148a · Cited by 201 · see citing works
Top-Down Beta Enhances Bottom-Up Gamma
Richter, C. G., Thompson, W. H., Bosman, C. A., & Fries, P. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3771-16.2017 · Cited by 195 · see citing works
Cooper, M., Griffith, R., Newman, J., Coil, A., Davis, M., Dutton, A., Faber, S., Guhathakurta, P., Koo, D., Lotz, J., et al. (2011) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2011.19938.x · Cited by 158 · see citing works
Integrating dispositions, signatures, and the interpersonal domain.
Fournier, M. A., Moskowitz, D. S., & Zuroff, D. C. (2008) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.94.3.531 · Cited by 196 · see citing works
Uranium in foraminiferal calcite as a recorder of seawater uranium concentrations
Russell, A. D., Emerson, S., Nelson, B. K., Erez, J., & Lea, D. W. (1994) · Geochimica et Cosmochimica Acta · doi:10.1016/0016-7037(94)90497-9 · Cited by 195 · see citing works
Statistics for the evaluation and comparison of models
(1986) · Deep Sea Research Part B Oceanographic Literature Review · doi:10.1016/0198-0254(86)91285-9 · Cited by 193 · see citing works
ENHANCEMENT OF INBREEDING DEPRESSION BY DOMINANCE AND SUPPRESSION IN IMPATIENS CAPENSIS
Schmitt, J., & Ehrhardt, D. W. (1990) · Evolution · doi:10.1111/j.1558-5646.1990.tb05197.x · Cited by 193 · see citing works
Aubert, O., Reese, P. P., Audry, B., Bouatou, Y., Raynaud, M., Viglietti, D., Legendre, C. M., Glotz, D., Empana, J. P., Jouven, X., et al. (2019) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2019.2322 · Cited by 192 · see citing works
The Use and Misuse of Structural Equation Modeling in Management Research: A Review and Critique
Davcik, N. S. (2010) · Journal of Advances in Management Research · doi:10.1108/jamr-07-2013-0043 · Cited by 202 · see citing works
CH4 production via CO2 reduction in a temperate bog: A source of 13C-depIeted CH4
Lansdown, J. M., Quay, P. D., & King, S. L. (1992) · Geochimica et Cosmochimica Acta · doi:10.1016/0016-7037(92)90393-w · Cited by 190 · see citing works
Hueber, T., Benaroya, E.-L., Chollet, G., Denby, B., Dreyfus, G., & Stone, M. (2009) · Speech Communication · doi:10.1016/j.specom.2009.11.004 · Cited by 186 · see citing works
Resolving an Ancient, Rapid Radiation in Saxifragales
Jian, S., Soltis, P. S., Gitzendanner, M. A., Moore, M. J., Li, R., Hendry, T. A., Qiu, Y., Dhingra, A., Bell, C. D., & Soltis, P. S. (2008) · Systematic Biology · doi:10.1080/10635150801888871 · Cited by 183 · see citing works
Marine protected area improves yield without disadvantaging fishers
Kerwath, S., Winker, H., Götz, A., & Attwood, C. G. (2013) · Nature Communications · doi:10.1038/ncomms3347 · Cited by 182 · see citing works
Intercorrelations of regional cerebral glucose metabolic rates in Alzheimer's disease
Horwitz, B., Grady, C. L., Schlageter, N. L., Duara, R., & Рапопорт, С. И. (1987) · Brain Research · doi:10.1016/0006-8993(87)91107-3 · Cited by 181 · see citing works
Forest biomass mapping from lidar and radar synergies
Sun, G., Ranson, K. J., Guo, Z., Zhang, Z., Montesano, P., & KIMES, D. S. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.03.021 · Cited by 179 · see citing works
Analyzing event-related EEG data with multivariate autoregressive parameters
Schlögl, A., & Supp, G. G. (2006) · Progress in brain research · doi:10.1016/s0079-6123(06)59009-0 · Cited by 175 · see citing works
Dunn, L. A., Atwater, G. E., & Kilts, C. D. (1993) · Psychopharmacology · doi:10.1007/bf02244927 · Cited by 171 · see citing works
DiClemente, R. J., Wingood, G. M., Rose, E., Sales, J. M., Lang, D. L., Caliendo, A. M., Hardin, J. W., & Crosby, R. A. (2009) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpediatrics.2009.205 · Cited by 169 · see citing works
Motor processes in simple, go/no-go, and choice reaction time tasks: A psychophysiological analysis.
Miller, J. O., & Low, K. A. (2001) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.27.2.266 · Cited by 170 · see citing works
Estimating forest attribute parameters for small areas using nearest neighbors techniques
McRoberts, R. E. (2011) · Forest Ecology and Management · doi:10.1016/j.foreco.2011.06.039 · Cited by 168 · see citing works
Wiley, E., Huepenbecker, S. P., Casper, B. B., & Helliker, B. R. (2013) · Tree Physiology · doi:10.1093/treephys/tpt093 · Cited by 167 · see citing works
Saa, P. A., & Nielsen, L. K. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.09.005 · Cited by 166 · see citing works
Evans, M., Khunti, K. K., Mamdani, M., Galbo-Jørgensen, C. B., Gundgaard, J., Bøgelund, M., & Harris, S. B. (2013) · Health and Quality of Life Outcomes · doi:10.1186/1477-7525-11-90 · Cited by 168 · see citing works
- Authors
- EFRON, B.
- Affiliations
- Stanford University (US)
- Corresponding author
- EFRON, BRADLEY
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1981
- Volume
- 68
- Issue
- 3
- Pages
- 589-599
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2006820723
- Cited by
- 1,399
- References
- 16
- Citations by year
- 2026: 23 · 2025: 32 · 2024: 46 · 2023: 28 · 2022: 59 · 2021: 65 · 2020: 66 · 2019: 57 · 2018: 94 · 2017: 54 · 2016: 69 · 2015: 79 · 2014: 60 · 2013: 55 · 2012: 73
- FWCI
- 7.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrika (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- standard error estimation, nonparametric statistics, jackknife, bootstrapping, subsampling, balanced repeated replication, influence function, delta method, correlation coefficient, variance estimation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review