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Journal articles on the topic 'Kolmogorov-Smirnov test'

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Published: 4 June 2021
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1

Reschenhofer, Erhard. "Generalization of the Kolmogorov-Smirnov test." Computational Statistics & Data Analysis 24, no. 4 (June 1997): 433–41. http://dx.doi.org/10.1016/s0167-9473(96)00077-1.

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2

Kim, Yoonji, Geunbae Kim, Unseob Jung, and Dongweon Yoon. "Blind Interleaver Parameter Estimation Using Kolmogorov-Smirnov Test." Journal of Korean Institute of Communications and Information Sciences 45, no. 3 (March 31, 2020): 584–92. http://dx.doi.org/10.7840/kics.2020.45.3.584.

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3

Ahn, Seongjin, Jaeyoon Lee, Junwon Choi, and Dongweon Yoon. "Improved Modulation Classification Algorithm Based on Kolmogorov-Smirnov Test." Journal of Korean Institute of Information Technology 15, no. 12 (December 21, 2017): 131–38. http://dx.doi.org/10.14801/jkiit.2017.15.12.131.

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4

Steinskog, Dag J., Dag B. Tjøstheim, and Nils G. Kvamstø. "A Cautionary Note on the Use of the Kolmogorov–Smirnov Test for Normality." Monthly Weather Review 135, no. 3 (March 1, 2007): 1151–57. http://dx.doi.org/10.1175/mwr3326.1.

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Abstract The Kolmogorov–Smirnov goodness-of-fit test is used in many applications for testing normality in climate research. This note shows that the test usually leads to systematic and drastic errors. When the mean and the standard deviation are estimated, it is much too conservative in the sense that its p values are strongly biased upward. One may think that this is a small sample problem, but it is not. There is a correction of the Kolmogorov–Smirnov test by Lilliefors, which is in fact sometimes confused with the original Kolmogorov–Smirnov test. Both the Jarque–Bera and the Shapiro–Wilk tests for normality are good alternatives to the Kolmogorov–Smirnov test. A power comparison of eight different tests has been undertaken, favoring the Jarque–Bera and the Shapiro–Wilk tests. The Jarque–Bera and the Kolmogorov–Smirnov tests are also applied to a monthly mean dataset of geopotential height at 500 hPa. The two tests give very different results and illustrate the danger of using the Kolmogorov–Smirnov test.
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5

., M. Arshad, M. T. Rasool ., and M. I. Ahmad . "Kolmogorov Smirnov Test for Generalized Pareto Distribution." Journal of Applied Sciences 2, no. 4 (March 15, 2002): 488–90. http://dx.doi.org/10.3923/jas.2002.488.490.

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6

Cong, Zicun, Lingyang Chu, Yu Yang, and Jian Pei. "Comprehensible counterfactual explanation on Kolmogorov-Smirnov test." Proceedings of the VLDB Endowment 14, no. 9 (May 2021): 1583–96. http://dx.doi.org/10.14778/3461535.3461546.

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The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test. In this paper, we tackle the problem of producing counterfactual explanations for test data failing the KS test. Concept-wise, we propose the notion of most comprehensible counterfactual explanations, which accommodates both the KS test data and the user domain knowledge in producing explanations. Computation-wise, we develop an efficient algorithm MOCHE (for <u>MO</u>st <u>C</u>ompre<u>H</u>ensible <u>E</u>xplanation) that avoids enumerating and checking an exponential number of subsets of the test set failing the KS test. MOCHE not only guarantees to produce the most comprehensible counterfactual explanations, but also is orders of magnitudes faster than the baselines. Experiment-wise, we present a systematic empirical study on a series of benchmark real datasets to verify the effectiveness, efficiency and scalability of most comprehensible counterfactual explanations and MOCHE.
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7

Böhm, Walter, and Kurt Hornik. "A Kolmogorov-Smirnov Test for r Samples." Fundamenta Informaticae 117, no. 1-4 (2012): 103–25. http://dx.doi.org/10.3233/fi-2012-690.

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8

Weber, Michael D., Lawrence M. Leemis, and Rex K. Kincaid. "Minimum Kolmogorov–Smirnov test statistic parameter estimates." Journal of Statistical Computation and Simulation 76, no. 3 (March 2006): 195–206. http://dx.doi.org/10.1080/00949650412331321098.

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9

Press, William H., and Saul A. Teukolsky. "Kolmogorov-Smirnov Test for Two-Dimensional Data." Computers in Physics 2, no. 4 (1988): 74. http://dx.doi.org/10.1063/1.4822753.

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10

Olea, Ricardo A., and Vera Pawlowsky-Glahn. "Kolmogorov–Smirnov test for spatially correlated data." Stochastic Environmental Research and Risk Assessment 23, no. 6 (July 29, 2008): 749–57. http://dx.doi.org/10.1007/s00477-008-0255-1.

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11

Vrbik, Jan. "Deriving CDF of Kolmogorov-Smirnov Test Statistic." Applied Mathematics 11, no. 03 (2020): 227–46. http://dx.doi.org/10.4236/am.2020.113018.

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12

Frommert, Mona, Ruth Durrer, and Jérôme Michaud. "The Kolmogorov-Smirnov test for the CMB." Journal of Cosmology and Astroparticle Physics 2012, no. 01 (January 3, 2012): 009. http://dx.doi.org/10.1088/1475-7516/2012/01/009.

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13

Drezner, Zvi, Ofir Turel, and Dawit Zerom. "A Modified Kolmogorov–Smirnov Test for Normality." Communications in Statistics - Simulation and Computation 39, no. 4 (March 31, 2010): 693–704. http://dx.doi.org/10.1080/03610911003615816.

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14

Otsu, Taisuke, and Go Taniguchi. "Kolmogorov–Smirnov type test for generated variables." Economics Letters 195 (October 2020): 109401. http://dx.doi.org/10.1016/j.econlet.2020.109401.

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15

Wee, Seungwoo, Changryoul Choi, and Jechang Jeong. "Blind Interleaver Parameters Estimation Using Kolmogorov–Smirnov Test." Sensors 21, no. 10 (May 15, 2021): 3458. http://dx.doi.org/10.3390/s21103458.

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The use of error-correcting codes (ECCs) is essential for designing reliable digital communication systems. Usually, most systems correct errors under cooperative environments. If receivers do not know interleaver parameters, they must first find out them to decode. In this paper, a blind interleaver parameters estimation method is proposed using the Kolmogorov–Smirnov (K–S) test. We exploit the fact that rank distributions of square matrices of linear codes differ from those of random sequences owing to the linear dependence of linear codes. We use the K–S test to make decision whether two groups are extracted from the same distribution. The K–S test value is used as a measure to find the most different rank distribution for the blind interleaver parameters estimation. In addition to control false alarm rates, multinomial distribution is used to calculate the probability that the most different rank distribution will occur. By exploiting those, we can estimate the interleaver period with relatively low complexity. Experimental results show that the proposed algorithm outperforms previous methods regardless of the bit error rate.
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16

Fasano, G., and A. Franceschini. "A multidimensional version of the Kolmogorov–Smirnov test." Monthly Notices of the Royal Astronomical Society 225, no. 1 (March 1987): 155–70. http://dx.doi.org/10.1093/mnras/225.1.155.

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17

Fu, Yongming, Jiang Zhu, Shilian Wang, and Zhipeng Xi. "Reduced Complexity SNR Estimation via Kolmogorov-Smirnov Test." IEEE Communications Letters 19, no. 9 (September 2015): 1568–71. http://dx.doi.org/10.1109/lcomm.2015.2450222.

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18

Bradley, Andrew P. "ROC curve equivalence using the Kolmogorov–Smirnov test." Pattern Recognition Letters 34, no. 5 (April 2013): 470–75. http://dx.doi.org/10.1016/j.patrec.2012.12.021.

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19

Khamis, H. J. "The two-stage i -corrected Kolmogorov-Smirnov test." Journal of Applied Statistics 27, no. 4 (May 2000): 439–50. http://dx.doi.org/10.1080/02664760050003623.

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20

Albano, A. M., P. E. Rapp, and A. Passamante. "Kolmogorov-Smirnov test distinguishes attractors with similar dimensions." Physical Review E 52, no. 1 (July 1, 1995): 196–206. http://dx.doi.org/10.1103/physreve.52.196.

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21

Burrell, Quentin L. "The Kolmogorov-Smirnov test and rank-frequency distributions." Journal of the American Society for Information Science 45, no. 1 (January 1994): 59. http://dx.doi.org/10.1002/(sici)1097-4571(199401)45:1<59::aid-asi7>3.0.co;2-i.

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22

Banerjee, Buddhananda, and Biswabrata Pradhan. "Kolmogorov–Smirnov test for life test data with hybrid censoring." Communications in Statistics - Theory and Methods 47, no. 11 (March 2, 2018): 2590–604. http://dx.doi.org/10.1080/03610926.2016.1205616.

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23

Oh, Hyungkook, and Haewoon Nam. "Numerical Approach with Kolmogorov-Smirnov Test for Detection of Impulsive Noise." Journal of Korea Information and Communications Society 39C, no. 9 (September 30, 2014): 852–60. http://dx.doi.org/10.7840/kics.2014.39c.9.852.

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24

Friedman, Yaakov, Alexander Gelman, and Eswar Phadia. "A modified kolmogorov-smirnov type goodness of fit test." Communications in Statistics - Theory and Methods 17, no. 12 (January 1988): 4147–62. http://dx.doi.org/10.1080/03610928808829864.

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25

Khamis, H. J. "The Δ-corrected kolmogorov-smirnov test with estimated parameters." Journal of Nonparametric Statistics 2, no. 1 (January 1992): 17–27. http://dx.doi.org/10.1080/10485259208832539.

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26

Wilcox, Rand R. "Some practical reasons for reconsidering the Kolmogorov-Smirnov test." British Journal of Mathematical and Statistical Psychology 50, no. 1 (May 1997): 9–20. http://dx.doi.org/10.1111/j.2044-8317.1997.tb01098.x.

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27

Grall-Maës, Edith. "Use of the Kolmogorov–Smirnov test for gamma process." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 226, no. 6 (October 17, 2012): 624–34. http://dx.doi.org/10.1177/1748006x12462522.

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28

Wang, Fanggang, and Xiaodong Wang. "Fast and Robust Modulation Classification via Kolmogorov-Smirnov Test." IEEE Transactions on Communications 58, no. 8 (August 2010): 2324–32. http://dx.doi.org/10.1109/tcomm.2010.08.090481.

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29

Zhang, Guowei, Xiaodong Wang, Ying-Chang Liang, and Ju Liu. "Fast and Robust Spectrum Sensing via Kolmogorov-Smirnov Test." IEEE Transactions on Communications 58, no. 12 (December 2010): 3410–16. http://dx.doi.org/10.1109/tcomm.2010.11.090209.

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30

Justel, Ana, Daniel Peña, and Rubén Zamar. "A multivariate Kolmogorov-Smirnov test of goodness of fit." Statistics & Probability Letters 35, no. 3 (October 1997): 251–59. http://dx.doi.org/10.1016/s0167-7152(97)00020-5.

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31

Sakamoto, Naoshi. "A Generalized Fitting Algorithm Using the Kolmogorov-Smirnov Test." International Journal of Computer Theory and Engineering 9, no. 2 (2017): 142–46. http://dx.doi.org/10.7763/ijcte.2017.v9.1127.

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32

Scaillet, Olivier. "A kolmogorov-smirnov type test for positive quadrant dependence." Canadian Journal of Statistics 33, no. 3 (September 2005): 415–27. http://dx.doi.org/10.1002/cjs.5540330307.

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33

Upomo, Togani Cahyadi, and Rini Kusumawardani. "PEMILIHAN DISTRIBUSI PROBABILITAS PADA ANALISA HUJAN DENGAN METODE GOODNESS OF FIT TEST." Jurnal Teknik Sipil dan Perencanaan 18, no. 2 (October 15, 2016): 139–48. http://dx.doi.org/10.15294/jtsp.v18i2.7480.

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Rainfall event is a stochastic process, so to explain and analyze this processes the probability theory and frequency analysisare used. There are four types of probability distributions.They are normal, log normal, log Pearson III and Gumbel. To find the best probabilities distribution, it will used goodness of fit test. The tests consist of chi-square and smirnov-kolmogorov. Results of the chi-square test for normal distribution, log normal and log Pearson III was 0.200, while for the Gumbel distribution was 2.333. Results of Smirnov Kolmogorov test for normal distribution D = 0.1554, log-normal distribution D = 0.1103, log Pearson III distribution D = 0.1177 and Gumbel distribution D = 0.095. All of the distribution can be accepted with a confidence level of 95%, but the best distribution is log normal distribution.Kejadian hujan merupakan proses stokastik, sehingga untuk keperluan analisa dan menjelaskan proses stokastik tersebut digunakan teori probabilitas dan analisa frekuensi. Terdapat empat jenis distribusi probabilitas yaitu distribusi normal, log normal, log pearson III dan gumbel. Untuk mencari distribusi probabilitas terbaik maka akan digunakan pengujian metode goodness of fit test. Pengujian tersebut meliputi uji chi-kuadrat dan uji smirnov kolmogorov. Hasil pengujian chi kuadrat untuk distribusi normal, log normal dan log pearson III adalah 0.200, sedangkan untuk distribusi gumbel 2.333. Hasil pengujian smirnov kolmogorov untuk distribusi normal dengan nilai D = 0.1554, distribusi log normal dengan nilai D = 0.1103, distribusi log pearson III dengan nilai D = 0.1177 dan distribusi gumbel dengan nilai D = 0.095. Seluruh distribusi dapat diterima dengan tingkat kepercayaan 95%, tetapi distribusi terbaik adalah distribusi log normal.
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34

Sinitsyn, Vyacheslav Yu, and Ekaterina S. Stupakova. "EMPIRICAL STUDY OF THE POWER OF THE KOLMOGOROV–SMIRNOV STATISTICAL TEST IN PROBLEMS OF TESTING HYPOTHESES ABOUT THE DISTRIBUTION LAW." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 3 (2022): 96–120. http://dx.doi.org/10.28995/2686-679x-2022-3-96-120.

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Statistical tests that use A.N. Kolmogorov one-sample statistics and N.V. Smirnov two-sample statistics have been widely used for solving applied problems for almost a hundred years. Those criteria are present in many textbooks and implemented in computer software for data analysis. The purpose of the work is to empirically estimate the power of the Kolmogorov–Smirnov criterion on a set of test problems to check hypotheses about the distribution law, as well as to investigate the properties of estimates of the power of the criterion for various types of resampling. To obtain various estimates of the power of the Kolmogorov–Smirnov criterion in solving test problems, the classical bootstrap, nonparametric bootstrap, parametric bootstrap, bootstrap with a random term, and resampling without returns were used. Based on the results of multiple solution of test problems, medians of p-values, medians of estimates of the power of the Kolmogorov–Smirnov criterion were calculated, and medians of biases of estimates on test problems and medians of all pairwise differences of various estimates of the power of the criterion were found. The distribution laws for the considered Kolmogorov–Smirnov power estimates, for the biases of the power estimates, and for all pairwise differences of the power estimates were investigated. For test problems where real data were considered, calculating power estimates without resampling is impossible, but unbiased power estimates can be predicted as the half-sum of two other power estimates, one obtained with a nonparametric bootstrap and the other obtained with resampling without returns. Methods for restructuring sample data and software for estimating the power of a statistical test developed within the study are universal and can be used for analyzing the properties of power estimates of other one-sample statistical tests, as well as for developing the classical methodology for checking statistical hypotheses.
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35

Al-Labadi, L., and M. Zarepour. "Two-sample Kolmogorov-Smirnov test using a Bayesian nonparametric approach." Mathematical Methods of Statistics 26, no. 3 (July 2017): 212–25. http://dx.doi.org/10.3103/s1066530717030048.

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36

Chen, Tzong-Jer, Keh-Shih Chuang, Wei Wu, and Yue-Ran Lu. "Compressed Medical Image Quality Determination Using the Kolmogorov- Smirnov Test." Current Medical Imaging Reviews 13, no. 2 (April 26, 2017): 204–9. http://dx.doi.org/10.2174/1573405612666160826095342.

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37

Feltz, Carol J. "Customizing Generalizations of the Kolmogorov-Smirnov Goodness-of-fit Test." Journal of Statistical Computation and Simulation 72, no. 2 (January 2002): 179–86. http://dx.doi.org/10.1080/00949650212143.

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38

Lopes, R. H. C., P. R. Hobson, and I. D. Reid. "Computationally efficient algorithms for the two-dimensional Kolmogorov–Smirnov test." Journal of Physics: Conference Series 119, no. 4 (July 1, 2008): 042019. http://dx.doi.org/10.1088/1742-6596/119/4/042019.

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39

Khamis, H. J. "A comparative study of the &-corrected Kolmogorov-Smirnov test." Journal of Applied Statistics 20, no. 3 (January 1993): 401–21. http://dx.doi.org/10.1080/02664769300000040.

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40

Koul, H. L., and P. K. Sen. "On a Kolmogorov-Smirnov type aligned test in linear regression." Statistics & Probability Letters 3, no. 3 (June 1985): 111–15. http://dx.doi.org/10.1016/0167-7152(85)90046-x.

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41

Khamis, H. J. "The δ-corrected Kolmogorov-Smirnov test for goodness of fit." Journal of Statistical Planning and Inference 24, no. 3 (March 1990): 317–35. http://dx.doi.org/10.1016/0378-3758(90)90051-u.

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42

Schindler, Martin. "Kolmogorov-Smirnov two-sample test based on regression rank scores." Applications of Mathematics 53, no. 4 (July 30, 2008): 297–304. http://dx.doi.org/10.1007/s10492-008-0027-8.

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43

Mason, David M., and John H. Schuenemeyer. "Correction: A Modified Kolmogorov-Smirnov Test Sensitive to Tail Alternatives." Annals of Statistics 20, no. 1 (March 1992): 620–21. http://dx.doi.org/10.1214/aos/1176348549.

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44

Hesamian, Gholamreza, and Jalal Chachi. "Two-sample Kolmogorov–Smirnov fuzzy test for fuzzy random variables." Statistical Papers 56, no. 1 (November 9, 2013): 61–82. http://dx.doi.org/10.1007/s00362-013-0566-2.

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45

Oak, Myung Hoon, and Jun Geol Baek. "Root Cause Analysis Methods in Semiconductor Manufacturing System Using Modified K-S(Kolmogorov-Smirnov) Test." Journal of the Korean Institute of Industrial Engineers 44, no. 2 (April 30, 2018): 132–40. http://dx.doi.org/10.7232/jkiie.2018.44.2.132.

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46

Tanguep, E. D. Wandji, and D. A. Njamen Njomen. "Kolmogorov-Smirnov APF Test for Inhomogeneous Poisson Processes with Shift Parameter." Applied Mathematics 12, no. 04 (2021): 322–35. http://dx.doi.org/10.4236/am.2021.124023.

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47

Feltz, Carol J. "Generalizations of the delta-Corrected Kolmogorov-Smirnov Goodness-of-Fit Test." Australian New Zealand Journal of Statistics 40, no. 4 (December 1998): 407–13. http://dx.doi.org/10.1111/1467-842x.00045.

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48

Calitz, Fred. "An alternative to the kolmogorov-smirnov test for goodness of fit." Communications in Statistics - Theory and Methods 16, no. 12 (January 1987): 3519–34. http://dx.doi.org/10.1080/03610928708829588.

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49

Denuit, Michel, Anne-Cécile Goderniaux, and Olivier Scaillet. "A Kolmogorov–Smirnov-Type Test for Shortfall Dominance Against Parametric Alternatives." Technometrics 49, no. 1 (February 2007): 88–99. http://dx.doi.org/10.1198/004017006000000309.

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50

Feltz, Carol J., and Gerald A. Goldin. "Generalization of the kolmogorov-smirnov goodness-of-fit test, usinggroup invariance." Journal of Nonparametric Statistics 1, no. 4 (January 1992): 357–70. http://dx.doi.org/10.1080/10485259208832535.

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