The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
The Kolmogorov-Smirnov Test for Goodness of Fit
Abstract
The test is based on the maximum difference between an empirical and a hypothetical cumulative distribution. Percentage points are tabled, and a lower bound to the power function is charted. Confidence limits for a cumulative distribution are described. Examples are given. Indications that the test is superior to the chi-square test are cited.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“A Kolmogorov-Smirnov test [42] rejected the normality assumption”
From Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild (2016) · cites this paper as [42]
Citations
Cites
Metropolis, N., & Ulam, S. (1949) · Journal of the American Statistical Association · doi:10.1080/01621459.1949.10483310 · Cited by 6,474
Table for Estimating the Goodness of Fit of Empirical Distributions
Smirnov, N. (1948) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730256 · Cited by 1,723
Heuristic Approach to the Kolmogorov-Smirnov Theorems
Doob, J. L. (1949) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729991 · Cited by 524
Giornale dell'Istituto italiano degli attuari
attuari, I. I. D. (1930) · Medical Entomology and Zoology · Cited by 449
On the Choice of the Number of Class Intervals in the Application of the Chi Square Test
Mann, H. B., & Wald, A. (1942) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731569 · Cited by 279
On the Kolmogorov-Smirnov Limit Theorems for Empirical Distributions
Feller, W. (1948) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730243 · Cited by 239
Non-parametric Statistical Inference
Wolfowitz, J. (1949) · Project Euclid (Cornell University) · Cited by 174
Confidence Limits for Continuous Distribution Functions
Wald, A., & Wolfowitz, J. (1939) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177732209 · Cited by 146
Statistical Inference in the Non-Parametric Case
Scheffé, H. (1943) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731355 · Cited by 97
The Choice of the Number and Width of Classes for the Chi-Square Test of Goodness of Fit
Williams, C. A. (1950) · Journal of the American Statistical Association · doi:10.1080/01621459.1950.10483336 · Cited by 85
Research works printed but not linked: 2
- Smirnov H., 1939, Recueil MathématiqueMatematiceskii Sbornik), 3
- Snedecor George W., 1946, Statistical Methods,
Cited by
Sorted by citations
Forouzanfar, M. H., Afshin, A., Alexander, L. T., Anderson, H. R., Bhutta, Z. A., Biryukov, S., Brauer, M., Burnett, R., Cercy, K., Charlson, F. J., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31679-8 · Cited by 7,698
On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown
Lilliefors, H. W. (1967) · Journal of the American Statistical Association · doi:10.2307/2283970 · Cited by 4,530
Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis
Ramsey, J. B. (1969) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1969.tb00796.x · Cited by 3,388
Gakidou, E., Afshin, A., Abajobir, A. A., Abate, K. H., Abbafati, C., Abbas, K. M., Abd-Allah, F., Abdulle, A. M., Abera, S. F., Aboyans, V., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)32366-8 · Cited by 2,406
A technology‐based global inventory of black and organic carbon emissions from combustion
Bond, T. C., Streets, D. G., Yarber, K. F., Nelson, S. M., Woo, J., & Klimont, Z. (2004) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2003jd003697 · Cited by 2,393
A Simple and Robust Statistical Test for Detecting the Presence of Recombination
Bruen, T. C., Philippe, H., & Bryant, D. (2006) · Genetics · doi:10.1534/genetics.105.048975 · Cited by 1,366
Deep Neural Networks and Tabular Data: A Survey
Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., & Kasneci, G. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3229161 · Cited by 1,400
Moustafa, N., & Slay, J. (2016) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2015.1125974 · Cited by 1,143
Solar forecasting methods for renewable energy integration
Inman, R. H., Pedro, H. T. C., & Coimbra, C. F. M. (2013) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2013.06.002 · Cited by 1,136
Learning To Count Objects in Images
Lempitsky, V., & Zisserman, A. (2010) · Cited by 1,063
Candidate Distributions for Climatological Drought Indices ( SPI and SPEI )
Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Loon, A. F. V., & Stahl, K. (2015) · International Journal of Climatology · doi:10.1002/joc.4267 · Cited by 877
Social Influence Bias: A Randomized Experiment
Muchnik, L., Aral, S., & Taylor, S. J. (2013) · Science · doi:10.1126/science.1240466 · Cited by 892
Youn, J., Dunham, W. H., Hong, S. J., Knight, J. D. R., Bashkurov, M., Chen, G. I., Bagci, H., Rathod, B., MacLeod, G. D. A. H., Eng, S. W. M., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2017.12.020 · Cited by 763
Sclater, J. G., & Francheteau, J. (1970) · Geophysical Journal International · doi:10.1111/j.1365-246x.1970.tb06089.x · Cited by 742
Patki, N., Wedge, R., & Veeramachaneni, K. K. (2016) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2016.49 · Cited by 716
On the Kolmogorov-Smirnov Test for the Exponential Distribution with Mean Unknown
Lilliefors, H. W. (1969) · Journal of the American Statistical Association · doi:10.1080/01621459.1969.10500983 · Cited by 637
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., & Song, D. (2018) · arXiv · doi:10.48550/arxiv.1802.08232 · Cited by 629
The Kolmogorov-Smirnov, Cramer-von Mises Tests
Darling, D. A. (1957) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177706788 · Cited by 599
Yang, D., Kleissl, J. P., Gueymard, C. A., Pedro, H. T. C., & Coimbra, C. F. M. (2018) · Solar Energy · doi:10.1016/j.solener.2017.11.023 · Cited by 584
Improved histograms for selectivity estimation of range predicates
Poosala, V., Haas, P. J., Ioannidis, Y., & Shekita, E. J. (1996) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/233269.233342 · Cited by 559
Evans, I. N., Primini, F. A., Glotfelty, K. J., Anderson, C. S., Bonaventura, N., Chen, J. C., Davis, J. E., Doe, S. M., Evans, J. D., Fabbiano, G., et al. (2010) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/189/1/37 · Cited by 551
Xu, H., Wang, Y., Guan, H., Shi, T., & Hu, X. (2019) · Remote Sensing · doi:10.3390/rs11202345 · Cited by 502
A Fully Automated Approach to Spike Sorting
Chung, J. E., Magland, J. F., Barnett, A. H., Tolosa, V., Tooker, A., Lee, K. Y., Shah, K. G., Felix, S. H., Frank, L. M., & Greengard, L. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.030 · Cited by 506
Reimers, N., & Gurevych, I. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1035 · Cited by 463
CHIME/FRB Discovery of Eight New Repeating Fast Radio Burst Sources
Andersen, B. C., Bandura, K., Bhardwaj, M., Boubel, P., Boyce, M. M., Boyle, P. J., Brar, C., Cassanelli, T., Chawla, P., Čubranić, D., et al. (2019) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ab4a80 · Cited by 440
What Do Single-View 3D Reconstruction Networks Learn?
Tatarchenko, M., Richter, S. R., Ranftl, R., Li, Z., Koltun, V., & Brox, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00352 · Cited by 443
Table of Percentage Points of Kolmogorov Statistics
Miller, L. H. (1956) · Journal of the American Statistical Association · doi:10.1080/01621459.1956.10501314 · Cited by 409
Alizadeh, M. R., Adamowski, J., Nikoo, M. R., AghaKouchak, A., Dennison, P. E., & Sadegh, M. (2020) · Science Advances · doi:10.1126/sciadv.aaz4571 · Cited by 378
A Molecular Framework for Auxin-Mediated Initiation of Flower Primordia
Yamaguchi, N., Wu, M., Winter, C. M., Berns, M. C., Nole-Wilson, S., Yamaguchi, A., Coupland, G., Krizek, B. A., & Wagner, D. (2013) · Developmental Cell · doi:10.1016/j.devcel.2012.12.017 · Cited by 379
Ince, R. A. A., Giordano, B. L., Kayser, C., Rousselet, G. A., Groß, J., & Schyns, P. G. (2016) · Human Brain Mapping · doi:10.1002/hbm.23471 · Cited by 363
Cayrou, C., Coulombe, P., Vigneron, A., Stanojčić, S., Ganier, O., Peiffer, I., Rivals, É., Puy, A., Laurent‐Chabalier, S., Desprat, R., et al. (2011) · Genome Research · doi:10.1101/gr.121830.111 · Cited by 359
Small vulnerable sets determine large network cascades in power grids
Yang, Y., Nishikawa, T., & Motter, A. E. (2017) · Science · doi:10.1126/science.aan3184 · Cited by 348
Improved Goodness-Of-Fit Tests
Finkelstein, J. M., & Schafer, R. E. (1971) · Biometrika · doi:10.1093/biomet/58.3.641 · Cited by 343
Clemons, P. A., Bodycombe, N. E., Carrinski, H. A., Wilson, J. A., Shamji, A. F., Wagner, B. K., Koehler, A. N., & Schreiber, S. L. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1012741107 · Cited by 343
Bernasconi, S. M., Daëron, M., Bergmann, K. D., Bonifacie, M., Meckler, A. N., Affek, H. P., Anderson, N., Bajnai, D., Barkan, E., Beverly, E., et al. (2021) · Geochemistry Geophysics Geosystems · doi:10.1029/2020gc009588 · Cited by 340
Protection against Cholera in Breast-Fed Children by Antibodies in Breast Milk
Glass, R. I., Svennerholm, A., Stoll, B. J., Khan, M. R. K., Hossain, K., Hug, M. I., & Holmgren, J. R. (1983) · New England Journal of Medicine · doi:10.1056/nejm198306093082304 · Cited by 339
World-trade web: Topological properties, dynamics, and evolution
Fagiolo, G., Reyes, J. A., & Schiavo, S. (2009) · Physical Review E · doi:10.1103/physreve.79.036115 · Cited by 346
Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild
Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.5960 · Cited by 333
Changes in observed climate extremes in global urban areas
Mishra, V. K., Ganguly, A. R., Nijssen, B., & Lettenmaier, D. P. (2015) · Environmental Research Letters · doi:10.1088/1748-9326/10/2/024005 · Cited by 344
Nonparametric Goodness-of-Fit Tests for Discrete Null Distributions
Taylor, B. A., & John, W. E. (2011) · The R Journal · doi:10.32614/rj-2011-016 · Cited by 317
Chapman, J. R., & Waldenström, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141853 · Cited by 329
Savage, I. R. (1957) · Journal of the American Statistical Association · doi:10.2307/2280903 · Cited by 307
Accelerating dynamics of collective attention
Lorenz-Spreen, P., Mønsted, B. M., Hövel, P., & Lehmann, S. (2019) · Nature Communications · doi:10.1038/s41467-019-09311-w · Cited by 304
Are Students Representatives of Professionals in Software Engineering Experiments?
Salman, I., Misirli, A. T., & Juristo, N. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.82 · Cited by 304
Qu, F. J., Sherwin, B. D., Madhavacheril, M. S., Han, D., Crowley, K. T., Abril-Cabezas, I., Ade, P. A. R., Aiola, S., Alford, T., Amiri, M., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/acfe06 · Cited by 394
Awais, M., Badruddin, N., & Drieberg, M. (2017) · Sensors · doi:10.3390/s17091991 · Cited by 301
Physician perspectives on integration of artificial intelligence into diagnostic pathology
Sarwar, S., Dent, A., Faust, K., Richer, M., Djuric, U., Van Ommeren, R., & Diamandis, P. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0106-0 · Cited by 297
Davis, J. E., Houck, J. C., & Nowak, M. A. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 294
Gender-specific pathway differences in the human serum metabolome
Krumsiek, J., Mittelstraß, K., Do, K. T., Stückler, F., Ried, J. S., Adamski, J., Peters, A., Illig, T., Kronenberg, F., Friedrich, N., et al. (2015) · Metabolomics · doi:10.1007/s11306-015-0829-0 · Cited by 293
Lotka's law: A testing procedure
Pao, M. L. (1985) · Information Processing & Management · doi:10.1016/0306-4573(85)90055-x · Cited by 291
Veldkamp, T., Zhao, F., Ward, P. J., de Moel, H., Aerts, J. C. J. H., Schmied, H. M., Portmann, F. T., Masaki, Y., POKHREL, Y. N., Liu, X., et al. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aab96f · Cited by 282
Reliability analysis of CNC machine tools
Keller, A. Z., Kamath, A. R. R., & Perera, U. D. (1982) · Reliability Engineering · doi:10.1016/0143-8174(82)90036-1 · Cited by 278
Cis and trans determinants of epigenetic silencing by Polycomb repressive complex 2 in Arabidopsis
Xiao, J., Jin, R., Yu, X., Shen, M. W., Wagner, J. D., Pai, A., Song, C. D., Zhuang, M., Klasfeld, S., He, C., et al. (2017) · Nature Genetics · doi:10.1038/ng.3937 · Cited by 276
Heterogeneous Defect Prediction
Nam, J., Fu, W., Kim, S., Menzies, T., & Tan, L. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2720603 · Cited by 269
Sengupta, M., Kurtz, S., Dobos, A. P., Wilbert, S., Lorenz, E., Renne, D. S., Myers, D. R., Wilcox, S. M., Blanc, P., & Perez, R. R. (2015) · doi:10.18777/ieashc-task46-2015-0001 · Cited by 268
A Cautionary Note on the Use of the Kolmogorov–Smirnov Test for Normality
Steinskog, D. J., Tjøstheim, D. B., & Kvamstø, N. G. (2007) · Monthly Weather Review · doi:10.1175/mwr3326.1 · Cited by 267
Quiroga, C. A., & Bullock, D. M. (1998) · Transportation Research Part C Emerging Technologies · doi:10.1016/s0968-090x(98)00010-2 · Cited by 266
Meta-analysis using effect size distributions of only statistically significant studies.
van Assen, M. A. L. M., van Aert, R. C. M., & Wicherts, J. M. (2015) · Psychological Methods · doi:10.1037/met0000025 · Cited by 265
(Mis)Information Dissemination in WhatsApp: Gathering, Analyzing and Countermeasures
Resende, G., Melo, P. D. F., Sousa, H., Messias, J., Vasconcelos, M., Almeida, J. M., & Benevenuto, F. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313688 · Cited by 258
Filling the Data Gaps Within GRACE Missions Using Singular Spectrum Analysis
Yi, S., & Sneeuw, N. (2021) · Journal of Geophysical Research Solid Earth · doi:10.1029/2020jb021227 · Cited by 257
DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning
Ni, P., Huang, N., Zhang, Z., Wang, D., Liang, F., Yu, M., Xiao, C., Luo, F., & Wang, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz276 · Cited by 259
Sheffer, L., Loewen, P. J., Soroka, S., Walgrave, S., & Sheafer, T. (2017) · American Political Science Review · doi:10.1017/s0003055417000569 · Cited by 261
Ojha, P. K., & Roy, K. (2011) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2011.08.007 · Cited by 250
Hydraulic fracturing volume is associated with induced earthquake productivity in the Duvernay play
Schultz, R., Atkinson, G. M., Eaton, D. W., Gu, Y. J., & Kao, H. (2018) · Science · doi:10.1126/science.aao0159 · Cited by 250
Modeling the Random Orientation of Mobile Devices: Measurement, Analysis and LiFi Use Case
Soltani, M. D., Purwita, A. A., Zeng, Z., Haas, H., & Safari, M. (2018) · IEEE Transactions on Communications · doi:10.1109/tcomm.2018.2882213 · Cited by 247
Reducing Features to Improve Code Change-Based Bug Prediction
Shivaji, S., Whitehead, E. J., Akella, R., & Kim, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.43 · Cited by 245
George, R., Chung, T. D., Vedam, S. S., Ramakrishnan, V., Mohan, R., Weiss, E., & Keall, P. (2006) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2006.02.035 · Cited by 243
Hype and heavy tails: A closer look at data breaches
Edwards, B., Hofmeyr, S. A., & Forrest, S. (2016) · Journal of Cybersecurity · doi:10.1093/cybsec/tyw003 · Cited by 239
Southern O cean eddy phenomenology
Frenger, I., Münnich, M., Gruber, N., & Knutti, R. (2015) · Journal of Geophysical Research Oceans · doi:10.1002/2015jc011047 · Cited by 240
Learning the Dynamics of Arterial Traffic From Probe Data Using a Dynamic Bayesian Network
Hofleitner, A., Herring, R., Abbeel, P., & Bayen, A. M. (2012) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2012.2200474 · Cited by 237
TERMINAL FLOWER 1-FD complex target genes and competition with FLOWERING LOCUS T
Zhu, Y., Klasfeld, S., Jeong, C. W., Jin, R., Goto, K., Yamaguchi, N., & Wagner, D. (2020) · Nature Communications · doi:10.1038/s41467-020-18782-1 · Cited by 243
A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication
Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 253
Numerical Tabulation of the Distribution of Kolmogorov's Statistic for Finite Sample Size
Birnbaum, Z. W. (1952) · Journal of the American Statistical Association · doi:10.2307/2281313 · Cited by 237
Testing experimental data for univariate normality
Henderson, A. R. (2006) · Clinica Chimica Acta · doi:10.1016/j.cca.2005.11.007 · Cited by 235
Papili Gao, N., Ud-Dean, S. M. M., Gandrillon, O., & Gunawan, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx575 · Cited by 235
A systematic study of key elements underlying molecular property prediction
Deng, J., Yang, Z., Wang, H., Ojima, I., Samaras, D., & Wang, F. (2023) · Nature Communications · doi:10.1038/s41467-023-41948-6 · Cited by 268
Liu, Y., Ruan, H., Wang, L., & Nehorai, A. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2627183 · Cited by 231
Aspect influences on soil water retention and storage
Geroy, I. J., Gribb, M. M., Marshall, H., Chandler, D. G., Benner, S. G., & McNamara, J. P. (2011) · Hydrological Processes · doi:10.1002/hyp.8281 · Cited by 230
Liu, Y., & Weisberg, R. H. (2011) · Journal of Geophysical Research Atmospheres · doi:10.1029/2010jc006837 · Cited by 228
Powerful Goodness-of-fit Tests Based on the Likelihood Ratio
Zhang, J. (2002) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00337 · Cited by 228
DeCost, B., Lei, B., Francis, T., & Holm, E. A. (2019) · Microscopy and Microanalysis · doi:10.1017/s1431927618015635 · Cited by 230
Huang, J., Korolkiewicz, M. W., Agrawal, M., & Boland, J. W. (2012) · Solar Energy · doi:10.1016/j.solener.2012.10.012 · Cited by 226
Kammerlander, N. H., Burger, D., Fust, A. P., & Fueglistaller, U. B. (2014) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2014.09.004 · Cited by 229
Primary Users in Cellular Networks: A Large-Scale Measurement Study
Willkomm, D., Sridhar Machiraju, B., Bolot, J. C., & Adam Wolisz, A. (2008) · IEEE International Symposium on Dynamic Spectrum Access Networks · doi:10.1109/dyspan.2008.48 · Cited by 222
Multifractality in combustion noise: predicting an impending combustion instability
Nair, V., & Sujith, R. I. (2014) · Journal of Fluid Mechanics · doi:10.1017/jfm.2014.171 · Cited by 218
Estimating Market Power and Strategies
Perloff, J. M., Karp, L. S., & Golan, A. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511753985 · Cited by 218
Dong, W., Moan, T., & Gao, Z. Q. (2012) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2012.06.011 · Cited by 215
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
Middlehurst, M., Schäfer, P., & Bagnall, A. J. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01022-1 · Cited by 263
Simulation-based framework to improve patient experience in an emergency department
Abo-Hamad, W., & Arisha, A. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2012.07.028 · Cited by 214
Statistics of the geomagnetic secular variation for the past 5 m.y.
Constable, C. G., & Parker, R. L. (1988) · Journal of Geophysical Research Atmospheres · doi:10.1029/jb093ib10p11569 · Cited by 212
Condie, K. C., Aster, R. C., & van Hunen, J. (2016) · Geoscience Frontiers · doi:10.1016/j.gsf.2016.01.006 · Cited by 211
Nikolopoulos, E. I., Crema, S., Marchi, L., Marra, F., Guzzetti, F., & Borga, M. (2014) · Geomorphology · doi:10.1016/j.geomorph.2014.06.015 · Cited by 210
An analysis of the severity and incident duration of truck-involved freeway accidents
Golob, T. F., Recker, W. W., & Leonard, J. D. (1987) · Accident Analysis & Prevention · doi:10.1016/0001-4575(87)90023-6 · Cited by 209
Identifying significantly impacted pathways: a comprehensive review and assessment
Nguyen, T., Shafi, A., Nguyen, T., & Drăghici, S. (2019) · Genome biology · doi:10.1186/s13059-019-1790-4 · Cited by 219
Chang, Y.-Y., Chang, H.-T., Chi, H.-R., Chen, M.-H., & Deng, L.-N. (2012) · Technovation · doi:10.1016/j.technovation.2012.03.001 · Cited by 239
Assessing the Reliability of the Dynamics Reconstructed from Metadynamics
Salvalaglio, M., Tiwary, P., & Parrinello, M. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500040r · Cited by 210
Reticulate Evolution of the Rye Genome
Martis, M. M., Zhou, R., Haseneyer, G., Schmutzer, T., Vrána, J., Kubaláková, M., König, S., Kugler, K., Scholz, U., Hackauf, B., et al. (2013) · The Plant Cell · doi:10.1105/tpc.113.114553 · Cited by 211
Delay and Doppler Spreads of Nonstationary Vehicular Channels for Safety-Relevant Scenarios
Bernadó, L., Zemen, T., Tufvesson, F., Molisch, A. F., & Mecklenbräuker, C. F. (2013) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2013.2271956 · Cited by 203
Testing the Efficacy of an Integrative Model for Community Participation
Hung, K., Sirakaya-Turk, E., & Ingram, L. J. (2010) · Journal of Travel Research · doi:10.1177/0047287510362781 · Cited by 203
Metadata
- Authors
- Massey, F. J.
- Affiliations
- University of Oregon (US)
- Corresponding author
- Massey, Frank J.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1951-03
- Volume
- 46
- Issue
- 253
- Pages
- 68-78
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W4255375128
- Cited by
- 7,210
- References
- 12
- Citations by year
- 2026: 529 · 2025: 691 · 2024: 580 · 2023: 591 · 2022: 576 · 2021: 562 · 2020: 490 · 2019: 432 · 2018: 369 · 2017: 351 · 2016: 285 · 2015: 273 · 2014: 215 · 2013: 226 · 2012: 181
- FWCI
- 6.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- Kolmogorov-Smirnov test, goodness of fit, empirical distribution function, cumulative distribution function, percentage points, power function, confidence limits
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