Cited by 7,210Statistical Distribution Estimation and ApplicationsProbability and Statistical ResearchStatistical Methods and Applications

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

Massey, Frank J.·1951·Journal of the American Statistical Association

DOI 10.1080/01621459.1951.10500769

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1080/01621459.1951.10500769

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

The Monte Carlo Method
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

Show all 12 references

Research works printed but not linked: 2

Smirnov H., 1939, Recueil MathématiqueMatematiceskii Sbornik), 3
Snedecor George W., 1946, Statistical Methods,

Sorted by citations

Items 901–1,000 of 1,012

Genomic prediction applied to high-biomass sorghum for bioenergy production
de Oliveira, A. A., Pastina, M. M., de Souza, V. F., Parrella, R. A. D. C., Noda, R. W., Simeone, M. L. F., Schaffert, R. E., de Magalhães, J. V., Damasceno, C. M. B., & Margarido, G. R. A. (2018) · Molecular Breeding · doi:10.1007/s11032-018-0802-5 · Cited by 44
A framework for end-to-end deep learning-based anomaly detection in transportation networks
Davis, N., Raina, G., & Jagannathan, K. (2020) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2020.100112 · Cited by 50
Sensor faults classification for SHM systems using deep learning-based method with Tsfresh features
Liu, G., Li, L., Zhang, L., Li, Q., & Law, S. (2020) · Smart Materials and Structures · doi:10.1088/1361-665x/ab85a6 · Cited by 44
Toward a More Renewable Energy-Based LFC Under Random Packet Transmissions and Delays With Stochastic Generation and Demand
Panda, D. K., Das, S., & Townley, S. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3042570 · Cited by 44
Fatigue life assessment method of bogie frame with time-domain extrapolation for dynamic stress based on extreme value theory
Wang, Q., Zhou, J., Gong, D., Wang, T., & Sun, Y. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107829 · Cited by 44
R-Phycoerythrin from Colaconema formosanum (Rhodophyta), an Anti-Allergic and Collagen Promoting Material for Cosmeceuticals
Lee, P., Yeh, H., Lung, W. Q. C., Huang, J., Chen, Y.-J., Chen, B., Nan, F., & Lee, M. (2021) · Applied Sciences · doi:10.3390/app11209425 · Cited by 44
Observational Undersampling in Tropical Cyclones and Implications for Estimated Intensity
Uhlhorn, E. W., & Nolan, D. S. (2011) · Monthly Weather Review · doi:10.1175/mwr-d-11-00073.1 · Cited by 44
An Approach to Testing the Goodness of Fit of Incompletely Specified Distributions
Srinivasan, R. (1970) · Biometrika · doi:10.1093/biomet/57.3.605 · Cited by 44
Towards Understanding the Impact of Length in Web Search Result Summaries over a Speech-only Communication Channel
Trippas, J. R., Spina, D., Sanderson, M., & Cavedon, L. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767826 · Cited by 43
Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
Nikula, R.-P., Ruusunen, M., Keski-Rahkonen, J., Saarinen, L., & Fagerholm, F. (2021) · Machines · doi:10.3390/machines9020039 · Cited by 43
In-Situ Block Characterization of Jointed Rock Exposures Based on a 3D Point Cloud Model
Kong, D., Wu, F., Saroglou, C., Sha, P., & Li, B. (2021) · Remote Sensing · doi:10.3390/rs13132540 · Cited by 43
Probabilistic hazard assessment of landslide-induced river damming
Zeng, P., Wang, S., Sun, X., Fan, X., Li, T., Wang, D., Feng, B., & Zhu, X. (2022) · Engineering Geology · doi:10.1016/j.enggeo.2022.106678 · Cited by 43
Complementary or Substitutive? A Novel Deep Learning Method to Leverage Text-image Interactions for Multimodal Review Helpfulness Prediction
Xiao, S., Chen, G., Zhang, C., & Li, X. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118138 · Cited by 43
Extragalactic fast X-ray transient candidates discovered by Chandra (2014–2022)
Quirola-Vásquez, J., Bauer, F. E., Jonker, P. G., Brandt, W. N., Yang, G., Levan, A. J., Xue, Y., Eappachen, D., Camacho, E., Ravasio, M. E., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202345912 · Cited by 43
Testing for normality: a user’s (cautionary) guide
Gosselin, R. (2024) · Laboratory Animals · doi:10.1177/00236772241276808 · Cited by 43
Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength
Tak, M. S. N., Feng, Y., & Mahgoub, M. (2025) · Infrastructures · doi:10.3390/infrastructures10020026 · Cited by 43
An improved anomaly detection and diagnosis framework for mobile network operators
Nováczki, S. (2013) · International Conference on the Design of Reliable Communication Networks (DRCN) · Cited by 43
ANN-based GA for generating the sizing curve of stand-alone photovoltaic systems
Mellit, A. (2010) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2009.12.008 · Cited by 43
Developing a framework for statistical process control approaches in project management
Colin, J., & Vanhoucke, M. (2015) · International Journal of Project Management · doi:10.1016/j.ijproman.2015.03.014 · Cited by 43
Single Molecule Force Spectroscopy on Titin Implicates Immunoglobulin Domain Stability as a Cardiac Disease Mechanism*
Anderson, B. R., Bogomolovas, J., Labeit, S., & Granzier, H. L. M. (2013) · Journal of Biological Chemistry · doi:10.1074/jbc.m112.401372 · Cited by 43
Latitudinal Analysis of Rainfall Intensity and Mean Annual Precipitation in Chile
Pizarro, R., Valdés, R., García-Chevesich, P. A., Vallejos, C., Sangüesa, C., Morales, C., Balocchi, F., Abarza, A., & Fuentes, R. (2012) · Chilean journal of agricultural research · doi:10.4067/s0718-58392012000200014 · Cited by 43
Application of Scenario Tree Reduction Via Quadratic Process to Medium-Term Hydrothermal Scheduling Problem
Beltrán, F., de Oliveira, W., & Finardi, E. C. (2017) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2658444 · Cited by 43
Continuous authentication using one-class classifiers and their fusion
Kumar, R., Kundu, P. P., & Phoha, V. V. (2018) · IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) · doi:10.1109/isba.2018.8311467 · Cited by 43
Estimating rates of wetland loss using power-law functions
Serran, J. N., Creed, I. F., Ameli, A., & Aldred, D. (2017) · Wetlands · doi:10.1007/s13157-017-0960-y · Cited by 43
Evaluation and comparison of interpolated gauge rainfall data and gridded rainfall data in Florida, USA
Zhang, M., de Leon, C., & Migliaccio, K. W. (2018) · Hydrological Sciences Journal · doi:10.1080/02626667.2018.1444767 · Cited by 44
The health effects of individual characteristics and environmental factors in China: Evidence from the hierarchical linear model
Zhang, Y., Jin, Y.-L., & Zhu, T.-T. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.05.163 · Cited by 44
Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy
Davis, N., Raina, G., & Jagannathan, K. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2860925 · Cited by 43
The Flow Short Scale (FSS) Dimensionality and What MIMIC Shows on Heterogeneity and Invariance
Kyriazos, T. A., Stalikas, A., Prassa, K., Galanakis, M., Flora, K., & Chatzilia, V. (2018) · Psychology · doi:10.4236/psych.2018.96083 · Cited by 43
Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification
Mohammadimanesh, F., Salehi, B., Mahdianpari, M., Brisco, B., & Gill, E. W. (2019) · Remote Sensing · doi:10.3390/rs11050516 · Cited by 43
Free energy computations by minimization of Kullback–Leibler divergence: An efficient adaptive biasing potential method for sparse representations
Bilionis, I., & Koutsourelakis, P. (2012) · Journal of Computational Physics · doi:10.1016/j.jcp.2012.01.033 · Cited by 43
The Numerical Invariant Measure of Stochastic Differential Equations With Markovian Switching
Li, X., Ma, Q., Yang, H., & Yuan, C. (2018) · SIAM Journal on Numerical Analysis · doi:10.1137/17m1143927 · Cited by 43
Discourse Level Factors for Sentence Deletion in Text Simplification
Zhong, Y., Jiang, C., Xu, W. H., & Li, J. J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6520 · Cited by 43
L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant
Vajpayee, V., Becerra, V. M., Bausch, N., Deng, J., Shimjith, S. R., & Arul, A. J. (2021) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2021.3090526 · Cited by 43
The Role of a Tropopause Polar Vortex in the Generation of the January 2019 Extreme Arctic Outbreak
Lillo, S. P., Cavallo, S. M., Parsons, D. B., & Riedel, C. P. (2021) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-20-0285.1 · Cited by 43
Impact of warmer climate on Lake Geneva water-temperature profiles
Perroud, M., & Goyette, S. (2024) · Archive ouverte UNIGE (University of Geneva) · doi:10.60910/9kt2-rh29 · Cited by 43
Accurate contactless sleep apnea detection framework with signal processing and machine learning methods
Zhuang, Z., Wang, F., Yang, X., Zhang, L., Fu, C., Xu, J. M., Li, C., & Hong, H. (2022) · Methods · doi:10.1016/j.ymeth.2022.06.013 · Cited by 43
Privacy-aware collection of aggregate spatial data
Xie, H., Kulik, L., & Tanin, E. (2011) · Data & Knowledge Engineering · doi:10.1016/j.datak.2011.03.007 · Cited by 43
Viscoelastic Properties of the P17 and Adult Rat Brain From Indentation in the Coronal Plane
Elkin, B. S., & Morrison, B. (2013) · Journal of Biomechanical Engineering · doi:10.1115/1.4025386 · Cited by 43
Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial
Collis, J., Connor, A. J., Paczkowski, M., Kannan, P., Pitt‐Francis, J., Byrne, H. M., & Hubbard, M. E. (2017) · Bulletin of Mathematical Biology · doi:10.1007/s11538-017-0258-5 · Cited by 45
Stochastic Properties of Cross-Sectional Financial Data
Lee, C.-W. J. (1985) · Journal of Accounting Research · doi:10.2307/2490915 · Cited by 43
Urban link travel speed dataset from a megacity road network
Guo, F., Zhang, D., Dong, Y., & Guo, Z. (2019) · Scientific Data · doi:10.1038/s41597-019-0060-3 · Cited by 43
On a New Result on the Ratio Exponentiated General Family of Distributions with Applications
Bantan, R. A. R., Jamal, F., Chesneau, C., & Elgarhy, M. (2020) · Mathematics · doi:10.3390/math8040598 · Cited by 42
Climate changes impact on stormwater infrastructure design in Tucson Arizona
Tousi, E. G., O’Brien, W., Doulabian, S., & Toosi, A. S. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103014 · Cited by 42
Probabilistic seismic assessment of reinforced concrete bridges using simulated records
Ozsarac, V., Monteiro, R., & Calvi, G. M. (2021) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2021.1956551 · Cited by 42
A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties
Wang, X., Brownlee, A. E. I., Weiszer, M., Woodward, J. R., Mahfouf, M., & Chen, J. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103382 · Cited by 42
Marine food webs are more complex but less stable in sub-Antarctic (Beagle Channel, Argentina) than in Antarctic (Potter Cove, Antarctic Peninsula) regions
Rodríguez, I. D., Marina, T. I., Schloss, I. R., & Saravia, L. A. (2022) · Marine Environmental Research · doi:10.1016/j.marenvres.2022.105561 · Cited by 42
Determining the Critical Failure Factors for Industry 4.0: An Exploratory Sequential Mixed Method Study
Sony, M., Antony, J., Tortorella, G. L., McDermott, O., & Gutiérrez, L. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3159860 · Cited by 42
Generating realistic cyber data for training and evaluating machine learning classifiers for network intrusion detection systems
Chalé, M. W., & Bastian, N. D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117936 · Cited by 44
Development of Monte‐Carlo‐based stochastic scenarios to improve uncertainty modelling for optimal energy management of a renewable energy hub
Tavakoli, A., & Karimi, A. (2022) · IET Renewable Power Generation · doi:10.1049/rpg2.12671 · Cited by 42
A comprehensive implementation of the log, Box-Cox and log-sinh transformations for skewed and censored precipitation data
Huang, Z., Zhao, T., Lai, R., Tian, Y., & Yang, F. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.129347 · Cited by 42
Learnometrics
Ochôa, X. (2011) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2090116.2090117 · Cited by 42
Differential expression of breast cancer-associated genes between stage- and age-matched tumor specimens from African- and Caucasian-American Women diagnosed with breast cancer
Grunda, J. M., Steg, A. D., He, Q. P., Steciuk, M. R., Byan-Parker, S., Johnson, M. R., & Grizzle, W. E. (2012) · BMC Research Notes · doi:10.1186/1756-0500-5-248 · Cited by 42
Statistical channel modeling of wireless shallow water acoustic communications from experiment data
Zhang, J., Cross, J., & Zheng, Y. R. (2010) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2010.5680323 · Cited by 42
A statistical model for seismic hazard assessment of hydraulic‐fracturing‐induced seismicity
Hajati, T., Langenbruch, C., & Shapiro, S. A. (2015) · Geophysical Research Letters · doi:10.1002/2015gl066652 · Cited by 42
Differential Membrane Binding Mechanics of Synaptotagmin Isoforms Observed in Atomic Detail
Vermaas, J. V., & Tajkhorshid, E. (2016) · Biochemistry · doi:10.1021/acs.biochem.6b00468 · Cited by 43
Water Quality Characteristics and Integrated Assessment Based on Multistep Correlation Analysis in the Danjiangkou Reservoir, China
Tan, X. H. (2015) · Journal of Environmental Informatics · doi:10.3808/jei.201500296 · Cited by 42
Integrating nonstationary behaviors of typhoon and non-typhoon extreme rainfall events in East Asia
Son, C., Lee, T., & Kwon, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-04629-1 · Cited by 42
Probabilistic bridge weigh-in-motion
OBrien, E. J., Zhang, L., Zhao, H., & Hajializadeh, D. (2018) · Canadian Journal of Civil Engineering · doi:10.1139/cjce-2017-0508 · Cited by 42
Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization
Dabboor, M., Montpetit, B., & Howell, S. (2018) · Remote Sensing · doi:10.3390/rs10040594 · Cited by 42
Explicit approximations for nonlinear switching diffusion systems in finite and infinite horizons
Yang, H., & Li, X. (2018) · Journal of Differential Equations · doi:10.1016/j.jde.2018.04.052 · Cited by 42
Peak factor statistics of wind effects for hyperbolic paraboloid roofs
Rizzo, F., Barbato, M., & Sepe, V. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.06.106 · Cited by 42
Predicting Taxi and Uber Demand in Cities: Approaching the Limit of Predictability
Zhao, K., Khryashchev, D., & Vo, H. T. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2955686 · Cited by 44
Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls
Shemesh, O., Polak, P., Lundin, K. E. A., Sollid, L. M., & Yaari, G. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.627813 · Cited by 42
CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words
Liu, C., Xia, X., Lo, D., Liu, Z., Hassan, A. E., & Li, S. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3465403 · Cited by 48
Active acoustic leak detection and localization in a plastic pipe using time delay estimation
Kafle, M. D., Fong, S., & Narasimhan, S. (2021) · Applied Acoustics · doi:10.1016/j.apacoust.2021.108482 · Cited by 42
Anomalous reactive transport in porous media: Experiments and modeling
Edery, Y., Dror, I., Scher, H., & Berkowitz, B. (2015) · Physical Review E · doi:10.1103/physreve.91.052130 · Cited by 42
Inverse Burr distribution for extreme wind speed prediction: Genesis, identification and estimation
Chiodo, E., & Falco, P. D. (2016) · Electric Power Systems Research · doi:10.1016/j.epsr.2016.08.028 · Cited by 42
Affective Polarization in Online Climate Change Discourse on Twitter
Tyagi, A., Uyheng, J., & Carley, K. M. (2020) · 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381419 · Cited by 41
Characterization of wellbore microannuli
Fernandez, S. G., Matteo, E., Taha, M. R., & Stormont, J. C. (2018) · Journal of Natural Gas Science and Engineering · doi:10.1016/j.jngse.2018.12.003 · Cited by 41
Artificial-Molecule-Based Chemical Reaction Optimization for Flow Shop Scheduling Problem With Deteriorating and Learning Effects
Fu, Y., Zhou, M., Guo, X., & Qi, L. (2019) · IEEE Access · doi:10.1109/access.2019.2911028 · Cited by 41
Generating multivariate load states using a conditional variational autoencoder
Wang, C., Sharifnia, E., Gao, Z. Y., Tindemans, S. H., & Pálenský, P. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108603 · Cited by 41
Interactive effect of post-harvest processing method, roasting degree, and brewing method on coffee metabolite profiles
Kim, C., Park, S. J., Yu, J. S., & Lee, D. Y. (2022) · Food Chemistry · doi:10.1016/j.foodchem.2022.133749 · Cited by 41
Artificial neural network model to estimate the long-term carbonation depth of concrete exposed to natural environments
Majlesi, A., Koodiani, H. K., de Rincón, O. T., Montoya, A., Millano, V., Torres-Acosta, A. A., & Troconis, B. C. R. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106545 · Cited by 41
Comprehensive understanding of TCP Incast problem
Chen, W., Ren, F., Xie, J., Lin, C., Yin, K., & Baker, F. (2015) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2015.7218549 · Cited by 41
Genetic algorithm optimized distribution sampling test for M-QAM modulation classification
Zhu, Z., Aslam, M. W., & Nandi, A. K. (2013) · Signal Processing · doi:10.1016/j.sigpro.2013.05.024 · Cited by 41
On the effective permeability of a heterogeneous porous medium: the role of the geometric mean
Selvadurai, P. A., & Selvadurai, A. P. S. (2014) · The Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics · doi:10.1080/14786435.2014.913111 · Cited by 41
Meta-analysis of the role of p53 status in isogenic systems tested for sensitivity to cytotoxic antineoplastic drugs
Cimoli, G., Malacarne, D., Ponassi, R., Valenti, M., Alberti, S., & Parodi, S. (2004) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2004.10.001 · Cited by 41
A 3D contour based geometrical model generator for complex-shaped horticultural products
Rogge, S., Defraeye, T., Herremans, E., Verboven, P., & Nicolaï, B. M. (2015) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2015.02.006 · Cited by 41
Amplitude and dynamics of polarization-plane signaling in the central complex of the locust brain
Bockhorst, T., & Homberg, U. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00742.2014 · Cited by 41
Modelling Regime‐Specific Stock Price Volatility*
Alexander, C., & Lazar, E. (2009) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.2009.00563.x · Cited by 41
Understanding the locality effect in Twitter: measurement and analysis
Cuevas, R., Gonzalez, R., Cuevas, √., & Guerrero, C. (2013) · Personal and Ubiquitous Computing · doi:10.1007/s00779-013-0658-y · Cited by 41
Mobile app adoption in different life stages: An empirical analysis
Frey, R. M., Run-hua, X., & Ilic, A. (2017) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2017.01.006 · Cited by 48
Ultrasound Image Despeckling Based on Statistical Similarity
Baselice, F. (2017) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2017.05.006 · Cited by 41
Comparing Measurement Response and Inverted Results of Electrical Resistivity Tomography Instruments
Parsekian, A., Claes, N., Singha, K., Minsley, B. J., Carr, B. J., Voytek, E. B., Harmon, R. E., Kass, A., Carey, A. M., Thayer, D., et al. (2017) · Journal of Environmental and Engineering Geophysics · doi:10.2113/jeeg22.3.249 · Cited by 41
The Waterfall of Liberty
Nasr, M., Zolfaghari, H., & Houmansadr, A. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3134075 · Cited by 41
Comparison of missing value estimation techniques in rainfall data of Bangladesh
Jahan, F., Sinha, N. C., Rahman, M. M., Rahman, M. M., Mondal, M. S. H., & Islam, M. A. (2018) · Theoretical and Applied Climatology · doi:10.1007/s00704-018-2537-y · Cited by 41
Using Vine Copulas to Generate Representative System States for Machine Learning
Konstantelos, I., Sun, M., Tindemans, S. H., Issad, S., Panciatici, P., & Štrbac, G. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2859367 · Cited by 41
Investor tastes: Implications for asset pricing in the public debt market
Shafron, E. (2018) · Journal of Corporate Finance · doi:10.1016/j.jcorpfin.2018.08.006 · Cited by 41
Micromechanical testing and modelling of blast furnace slag cement pastes
Šavija, B., Zhang, H., & Schlangen, E. (2019) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.117841 · Cited by 41
Estimation of Surface Downward Shortwave Radiation over China from Himawari-8 AHI Data Based on Random Forest
Hou, N., Zhang, X., Zhang, W., Wei, Y., Jia, K., Yao, Y., Jiang, B., & Cheng, J. (2020) · Remote Sensing · doi:10.3390/rs12010181 · Cited by 41
Statistical point cloud model to investigate measurement uncertainty in coordinate metrology
Senin, N., Catalucci, S., Moretti, M., & Leach, R. (2021) · Precision Engineering · doi:10.1016/j.precisioneng.2021.01.008 · Cited by 41
Does agricultural intensification cause tipping points in ecosystem services?
Watson, S. C. L., Newton, A. C., Ridding, L. E., Evans, P. M., Brand, S., McCracken, M. E., Gosal, A. S., & Bullock, J. M. (2021) · Landscape Ecology · doi:10.1007/s10980-021-01321-8 · Cited by 41
Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control
Liu, H., Han, K., Gayah, V. V., Friesz, T. L., & Yao, T. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.05.016 · Cited by 41
Normalizing variables with too-frequent values using a Kolmogorov–Smirnov test: A practical approach
Drezner, Z., & Turel, O. (2011) · Computers & Industrial Engineering · doi:10.1016/j.cie.2011.07.015 · Cited by 41
Investigating spatial variation in the relationships between NDVI and environmental factors at multi-scales: a case study of Guizhou Karst Plateau, China
Gao, J., Li, S., Zhi, Z., & Cai, Y. (2011) · International Journal of Remote Sensing · doi:10.1080/01431161.2011.605811 · Cited by 41
Analysis of X-Band SAR Sea-Clutter Distributions at Different Grazing Angles
Fiche, A., Angelliaume, S., Rosenberg, L., & Khenchaf, A. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2405577 · Cited by 41
A History of Distribution Sampling Prior to the Era of the Computer and its Relevance to Simulation
Teichroew, D. (1965) · Journal of the American Statistical Association · doi:10.1080/01621459.1965.10480773 · Cited by 41
Stable Patterns of Allelic Diversity at the Merozoite Surface Protein‐1 Locus of Plasmodium falciparum in Clinical Isolates from Southern Vietnam
Ferreira, M. U., Liu, Q., Zhou, M., Kimura, M., Kaneko, O., Thien, H. V., Isomura, S., Tanabe, K., & Kawamoto, F. (1998) · Journal of Eukaryotic Microbiology · doi:10.1111/j.1550-7408.1998.tb05080.x · Cited by 41
Testing the assumptions of exponential, additive reaction time models
Ashby, F. G. (1982) · Memory & Cognition · doi:10.3758/bf03209213 · Cited by 41
Local Decision Pitfalls in Interactive Machine Learning: An Investigation into Feature Selection in Sentiment Analysis
Wu, T., Weld, D. S., & Heer, J. (2019) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3319616 · Cited by 39

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
DOI: 10.1080/01621459.1951.10500769
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

The Kolmogorov-Smirnov Test for Goodness of Fit

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks