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Gotowa bibliografia na temat „Kolmogorov-Smirnov test”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 20 lutego 2023

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Artykuły w czasopismach na temat "Kolmogorov-Smirnov test"

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Reschenhofer, Erhard. "Generalization of the Kolmogorov-Smirnov test". Computational Statistics & Data Analysis 24, nr 4 (czerwiec 1997): 433–41. http://dx.doi.org/10.1016/s0167-9473(96)00077-1.

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Kim, Yoonji, Geunbae Kim, Unseob Jung i Dongweon Yoon. "Blind Interleaver Parameter Estimation Using Kolmogorov-Smirnov Test". Journal of Korean Institute of Communications and Information Sciences 45, nr 3 (31.03.2020): 584–92. http://dx.doi.org/10.7840/kics.2020.45.3.584.

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Ahn, Seongjin, Jaeyoon Lee, Junwon Choi i Dongweon Yoon. "Improved Modulation Classification Algorithm Based on Kolmogorov-Smirnov Test". Journal of Korean Institute of Information Technology 15, nr 12 (21.12.2017): 131–38. http://dx.doi.org/10.14801/jkiit.2017.15.12.131.

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Steinskog, Dag J., Dag B. Tjøstheim i Nils G. Kvamstø. "A Cautionary Note on the Use of the Kolmogorov–Smirnov Test for Normality". Monthly Weather Review 135, nr 3 (1.03.2007): 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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., M. Arshad, M. T. Rasool . i M. I. Ahmad . "Kolmogorov Smirnov Test for Generalized Pareto Distribution". Journal of Applied Sciences 2, nr 4 (15.03.2002): 488–90. http://dx.doi.org/10.3923/jas.2002.488.490.

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Cong, Zicun, Lingyang Chu, Yu Yang i Jian Pei. "Comprehensible counterfactual explanation on Kolmogorov-Smirnov test". Proceedings of the VLDB Endowment 14, nr 9 (maj 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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Böhm, Walter, i Kurt Hornik. "A Kolmogorov-Smirnov Test for r Samples". Fundamenta Informaticae 117, nr 1-4 (2012): 103–25. http://dx.doi.org/10.3233/fi-2012-690.

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Weber, Michael D., Lawrence M. Leemis i Rex K. Kincaid. "Minimum Kolmogorov–Smirnov test statistic parameter estimates". Journal of Statistical Computation and Simulation 76, nr 3 (marzec 2006): 195–206. http://dx.doi.org/10.1080/00949650412331321098.

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Press, William H., i Saul A. Teukolsky. "Kolmogorov-Smirnov Test for Two-Dimensional Data". Computers in Physics 2, nr 4 (1988): 74. http://dx.doi.org/10.1063/1.4822753.

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Olea, Ricardo A., i Vera Pawlowsky-Glahn. "Kolmogorov–Smirnov test for spatially correlated data". Stochastic Environmental Research and Risk Assessment 23, nr 6 (29.07.2008): 749–57. http://dx.doi.org/10.1007/s00477-008-0255-1.

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Rozprawy doktorskie na temat "Kolmogorov-Smirnov test"

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Böhm, Walter, i Kurt Hornik. "A Kolmogorov-Smirnov Test for r Samples". WU Vienna University of Economics and Business, 2010. http://epub.wu.ac.at/2960/1/Report105.pdf.

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We consider the problem of testing whether r (>=2) samples are drawn from the same continuous distribution F(x). The test statistic we will study in some detail is defined as the maximum of the circular differences of the empirical distribution functions, a generalization of the classical 2-sample Kolmogorov-Smirnov test to r (>=2) independent samples. For the case of equal sample sizes we derive the exact null distribution by counting lattice paths confined to stay in the scaled alcove $\mathcal{A}_r$ of the affine Weyl group $A_{r-1}$. This is done using a generalization of the classical reflection principle. By a standard diffusion scaling we derive also the asymptotic distribution of the test statistic in terms of a multivariate Dirichlet series. When the sample sizes are not equal the reflection principle no longer works, but we are able to establish a weak convergence result even in this case showing that by a proper rescaling a test statistic based on a linear transformation of the circular differences of the empirical distribution functions has the same asymptotic distribution as the test statistic in the case of equal sample sizes.
Series: Research Report Series / Department of Statistics and Mathematics
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Andrade, Francisco Arruda Raposo. "New techniques for vibration condition monitoring : Volterra kernel and Kolmogorov-Smirnov". Thesis, Brunel University, 1999. http://bura.brunel.ac.uk/handle/2438/7871.

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This research presents a complete review of signal processing techniques used, today, in vibration based industrial condition monitoring and diagnostics. It also introduces two novel techniques to this field, namely: the Kolmogorov-Smirnov test and Volterra series, which have not yet been applied to vibration based condition monitoring. The first technique, the Kolmogorov-Smirnov test, relies on a statistical comparison of the cumulative probability distribution functions (CDF) from two time series. It must be emphasised that this is not a moment technique, and it uses the whole CDF, in the comparison process. The second tool suggested in this research is the Volterra series. This is a non-linear signal processing technique, which can be used to model a time series. The parameters of this model are used for condition monitoring applications. Finally, this work also presents a comprehensive comparative study between these new methods and the existing techniques. This study is based on results from numerical and experimental applications of each technique here discussed. The concluding remarks include suggestions on how the novel techniques proposed here can be improved.
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Steele, Michael C., i n/a. "The Power of Categorical Goodness-Of-Fit Statistics". Griffith University. Australian School of Environmental Studies, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20031006.143823.

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The relative power of goodness-of-fit test statistics has long been debated in the literature. Chi-Square type test statistics to determine 'fit' for categorical data are still dominant in the goodness-of-fit arena. Empirical Distribution Function type goodness-of-fit test statistics are known to be relatively more powerful than Chi-Square type test statistics for restricted types of null and alternative distributions. In many practical applications researchers who use a standard Chi-Square type goodness-of-fit test statistic ignore the rank of ordinal classes. This thesis reviews literature in the goodness-of-fit field, with major emphasis on categorical goodness-of-fit tests. The continued use of an asymptotic distribution to approximate the exact distribution of categorical goodness-of-fit test statistics is discouraged. It is unlikely that an asymptotic distribution will produce a more accurate estimation of the exact distribution of a goodness-of-fit test statistic than a Monte Carlo approximation with a large number of simulations. Due to their relatively higher powers for restricted types of null and alternative distributions, several authors recommend the use of Empirical Distribution Function test statistics over nominal goodness-of-fit test statistics such as Pearson's Chi-Square. In-depth power studies confirm the views of other authors that categorical Empirical Distribution Function type test statistics do not have higher power for some common null and alternative distributions. Because of this, it is not sensible to make a conclusive recommendation to always use an Empirical Distribution Function type test statistic instead of a nominal goodness-of-fit test statistic. Traditionally the recommendation to determine 'fit' for multivariate categorical data is to treat categories as nominal, an approach which precludes any gain in power which may accrue from a ranking, should one or more variables be ordinal. The presence of multiple criteria through multivariate data may result in partially ordered categories, some of which have equal ranking. This thesis proposes a modification to the currently available Kolmogorov-Smirnov test statistics for ordinal and nominal categorical data to account for situations of partially ordered categories. The new test statistic, called the Combined Kolmogorov-Smirnov, is relatively more powerful than Pearson's Chi-Square and the nominal Kolmogorov-Smirnov test statistic for some null and alternative distributions. A recommendation is made to use the new test statistic with higher power in situations where some benefit can be achieved by incorporating an Empirical Distribution Function approach, but the data lack a complete natural ordering of categories. The new and established categorical goodness-of-fit test statistics are demonstrated in the analysis of categorical data with brief applications as diverse as familiarity of defence programs, the number of recruits produced by the Merlin bird, a demographic problem, and DNA profiling of genotypes. The results from these applications confirm the recommendations associated with specific goodness-of-fit test statistics throughout this thesis.
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Steele, Michael C. "The Power of Categorical Goodness-Of-Fit Statistics". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366717.

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The relative power of goodness-of-fit test statistics has long been debated in the literature. Chi-Square type test statistics to determine 'fit' for categorical data are still dominant in the goodness-of-fit arena. Empirical Distribution Function type goodness-of-fit test statistics are known to be relatively more powerful than Chi-Square type test statistics for restricted types of null and alternative distributions. In many practical applications researchers who use a standard Chi-Square type goodness-of-fit test statistic ignore the rank of ordinal classes. This thesis reviews literature in the goodness-of-fit field, with major emphasis on categorical goodness-of-fit tests. The continued use of an asymptotic distribution to approximate the exact distribution of categorical goodness-of-fit test statistics is discouraged. It is unlikely that an asymptotic distribution will produce a more accurate estimation of the exact distribution of a goodness-of-fit test statistic than a Monte Carlo approximation with a large number of simulations. Due to their relatively higher powers for restricted types of null and alternative distributions, several authors recommend the use of Empirical Distribution Function test statistics over nominal goodness-of-fit test statistics such as Pearson's Chi-Square. In-depth power studies confirm the views of other authors that categorical Empirical Distribution Function type test statistics do not have higher power for some common null and alternative distributions. Because of this, it is not sensible to make a conclusive recommendation to always use an Empirical Distribution Function type test statistic instead of a nominal goodness-of-fit test statistic. Traditionally the recommendation to determine 'fit' for multivariate categorical data is to treat categories as nominal, an approach which precludes any gain in power which may accrue from a ranking, should one or more variables be ordinal. The presence of multiple criteria through multivariate data may result in partially ordered categories, some of which have equal ranking. This thesis proposes a modification to the currently available Kolmogorov-Smirnov test statistics for ordinal and nominal categorical data to account for situations of partially ordered categories. The new test statistic, called the Combined Kolmogorov-Smirnov, is relatively more powerful than Pearson's Chi-Square and the nominal Kolmogorov-Smirnov test statistic for some null and alternative distributions. A recommendation is made to use the new test statistic with higher power in situations where some benefit can be achieved by incorporating an Empirical Distribution Function approach, but the data lack a complete natural ordering of categories. The new and established categorical goodness-of-fit test statistics are demonstrated in the analysis of categorical data with brief applications as diverse as familiarity of defence programs, the number of recruits produced by the Merlin bird, a demographic problem, and DNA profiling of genotypes. The results from these applications confirm the recommendations associated with specific goodness-of-fit test statistics throughout this thesis.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Australian School of Environmental Studies
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Larson, Lincoln Gary. "Investigating Statistical vs. Practical Significance of the Kolmogorov-Smirnov Two-Sample Test Using Power Simulations and Resampling Procedures". Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28770.

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This research examines the power of the Kolmogorov-Smirnov two-sample test. The motivation for this research is a large data set containing soil salinity values. One problem encountered was that the power of the Kolmogorov-Smirnov two-sample test became extremely high due to the large sample size. This extreme power resulted in statistically significant differences between two distributions when no practically significant difference was present. This research used resampling procedures to create simulated null distributions for the test statistic. These null distributions were used to obtain power approximations for the Kolmogorov-Smirnov tests under differing effect sizes. The research shows that the power of the Kolmogorov-Smirnov test can become very large in cases of large sample sizes.
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Li, Rong. "A Tree-based Framework for Difference Summarization". Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1334277940.

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Mao, Qian. "Clusters Identification: Asymmetrical Case". Thesis, Uppsala universitet, Informationssystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-208328.

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Cluster analysis is one of the typical tasks in Data Mining, and it groups data objects based only on information found in the data that describes the objects and their relationships. The purpose of this thesis is to verify a modified K-means algorithm in asymmetrical cases, which can be regarded as an extension to the research of Vladislav Valkovsky and Mikael Karlsson in Department of Informatics and Media. In this thesis an experiment is designed and implemented to identify clusters with the modified algorithm in asymmetrical cases. In the experiment the developed Java application is based on knowledge established from previous research. The development procedures are also described and input parameters are mentioned along with the analysis. This experiment consists of several test suites, each of which simulates the situation existing in real world, and test results are displayed graphically. The findings mainly emphasize the limitations of the algorithm, and future work for digging more essences of the algorithm is also suggested.
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Carrier, Denis Joseph Gaston. "Automatic measurement of particles from holograms taken in the combustion chamber of a rocket motor". Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/22924.

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Approved for public release; distribution is unlimited
This thesis describes the procedure used for the automatic measurement of particles from hologram taken in the combustion chamber of a rocket motor while firing. It describes the investigation done on two averaging techniques used to reduce speckle noise, capturing the image focused on a spinning mylar disk and software averaging of several image frames. The spinning disk technique proved superior for this application. The Kolmogorov-Smirnov two-sample test is applied to different particle samples in order to find an estimate of the number of particles required to obtain a stable distribution function. The number of particles is calculated and given. The last part of this study shows real particle distributions in the form of frequency histograms.
http://archive.org/details/automaticmeasure00carr
Major, Canadian Armed Forces
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Lekomtcev, Demian. "Snímání spektra pro kognitivní rádiové sítě - vliv vlastností reálného komunikačního řetězce". Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255288.

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The doctoral thesis deals with spectrum sensing in cognitive radio networks (CRN). A number of international organizations are currently actively engaged in standardization of CRN and it points out to the fact that this technology will be widely used in the near future. One of the key features of this technology is a dynamic access to the spectrum, which can be affected by many different harmful factors occurring in the communication chain. The thesis investigates the influence of selected factors on the spectrum sensing process. Another contribution of the work is the optimization of the Kolmogorov - Smirnov statistical test that can be applied for the primary user signal detection. The work also incorporates the analysis of the influence of the harmful effects caused by the commonly used transmitters and receivers on various spectrum sensing methods. The investigations are verified by the results of the simulations and also by the measurements with experimental platforms based on the software-defined radio (SDR).
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Bagdonavičius, Vilijandas B., Ruta Levuliene, Mikhail S. Nikulin i Olga Zdorova-Cheminade. "Tests for homogeneity of survival distributions against non-location alternatives and analysis of the gastric cancer data". Universität Potsdam, 2004. http://opus.kobv.de/ubp/volltexte/2011/5152/.

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The two and k-sample tests of equality of the survival distributions against the alternatives including cross-effects of survival functions, proportional and monotone hazard ratios, are given for the right censored data. The asymptotic power against approaching alternatives is investigated. The tests are applied to the well known chemio and radio therapy data of the Gastrointestinal Tumor Study Group. The P-values for both proposed tests are much smaller then in the case of other known tests. Differently from the test of Stablein and Koutrouvelis the new tests can be applied not only for singly but also to randomly censored data.
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Książki na temat "Kolmogorov-Smirnov test"

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Wiesen, Christopher. Learn to Use the Kolmogorov–Smirnov Test in Stata With the Cardiac Catheterization Diagnostic Dataset (2018). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526489302.

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Scott Jones, Julie. Learn to Use the Kolmogorov–Smirnov Test in SPSS With Data From the Opinions and Lifestyle Survey (Well-Being Module) (2015). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526486721.

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Scott Jones, Julie. Learn to Use the Kolmogorov–Smirnov Test in R With Data From the Opinions and Lifestyle Survey (Well-Being Module) (2015). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526497062.

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United States. National Aeronautics and Space Administration., red. Final report for two XTE A01 projects: A multifrequency study of Circinus X-1 and A search for microsecond variability from bright galactic X-ray source. [Washington, DC: National Aeronautics and Space Administration, 1998.

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dos Santos Chaves, Lígia Gizely, i Ricardo Figueiredo Pinto. Estudo do perfil socioeconômico, estado nutricional, nível de mercúrio e desenvolvimento motor em crianças da Ilha de Cotijuba, Pará / Brasil. Redaktorzy Robson Antonio Tavares Costa, Carlos Gomes de Carvalho Leitão i Carla Sandra Carneiro Afonso. Conhecimento e ciência, 2019. http://dx.doi.org/10.20872/estudodoperfilsocieconomico.

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Este estudo objetiva avaliar o desenvolvimento motor, estado nutricional, nível de mercúrio e perfil socioeconômico de crianças residentes na Ilha de Cotijuba, Pará, Brasil, na faixa etária de 5 a 10 anos. Metodologicamente, o desenvolvimento motor foi avaliado através do protocolo TGMD – 2 (Test of Gross Motor Development, Secunde Edition). A avaliação do estado nutricional da amostra, utilizou o software WHO AntroPlus. A análise de mercúrio foi realizada através da metodologia de redução e espectrometria de absorção atômica por vapor frio (CVAAS) (sistema aberto de circulação do fluxo de ar), utilizando um detector de mercúrio automático, o Automatic Mercury analyzer Model HG-201 da Sanso Seisakusho Co. Ltd., Tokyo, Japan. Para a avaliação socioeconômica da amostra, foi utilizado o instrumento CCEB 2014 (Critério de Classificação Econômica Brasil 2014) da ABEP. A análise dos dados foi realizada pelo programa SPSS 22.0, em que se adotou a estatística descritiva de tendência central, não central e dispersão e para a estatística inferencial primeiramente foi realizado o teste de Kolmogorov-Smirnov para normalidade da amostra, e o Teste t independente para comparar as médias da amostra de acordo com o sexo e grupo etário. O teste de correlação de Pearson foi utilizado para analisar o grau de associação entre as variáveis. Foi adotado um nível de significância p≤0,05. O desenvolvimento motor encontrou-se na média em sua maioria, seguido por abaixo da média e, posteriormente, acima da média. Quanto ao estado nutricional, a maioria apresentou peso adequado para a idade, estatura adequada para idade e eutrofia com o aparecimento de alguns casos de sobrepeso e obesidade com o avançar da idade. O nível de exposição ao mercúrio encontrou-se abaixo do limite de tolerância biológico, com diferença significativa entre sexo. O perfil socioeconômico das famílias, na sua maioria, perteceu às classes C2 e D. A renda média bruta das famílias, majoritariamente, apresentou-se entre R$ 1277,00 e R$ 895,00. Conclui-se que a avaliação do desenvolvimento motor e os fatores intervenientes a este estão na normalidade. Análises similares devem ocorrer periodicamente para planejamento legislativo e projetos de intervenção em educação e saúde.
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Części książek na temat "Kolmogorov-Smirnov test"

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Gooch, Jan W. "Kolmogorov-Smirnov Test". W Encyclopedic Dictionary of Polymers, 984. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15267.

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Lopes, Raul H. C. "Kolmogorov-Smirnov Test". W International Encyclopedia of Statistical Science, 718–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_326.

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Demidenko, Eugene. "Kolmogorov-Smirnov Test for Image Comparison". W Computational Science and Its Applications – ICCSA 2004, 933–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24768-5_100.

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Destercke, Sébastien, i Olivier Strauss. "Kolmogorov-Smirnov Test for Interval Data". W Information Processing and Management of Uncertainty in Knowledge-Based Systems, 416–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08852-5_43.

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Wang, Zhixiong, i Wei Wang. "Concept Drift Detection Based on Kolmogorov–Smirnov Test". W Lecture Notes in Electrical Engineering, 273–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0187-6_31.

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Grzegorzewski, Przemyslaw, i Maciej Romaniuk. "Bootstrapped Kolmogorov-Smirnov Test for Epistemic Fuzzy Data". W Information Processing and Management of Uncertainty in Knowledge-Based Systems, 494–507. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08974-9_39.

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Georgiev, George, Iren Valova i Natacha Gueorguieva. "Binary Tree Classifier Based on Kolmogorov-Smirnov Test". W Advances in Intelligent Decision Technologies, 571–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14616-9_55.

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Lin, Pei-Chun, Berlin Wu i Junzo Watada. "Kolmogorov-Smirnov Two Sample Test with Continuous Fuzzy Data". W Advances in Intelligent and Soft Computing, 175–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11960-6_17.

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Abachi, Hadi Mohammadzadeh, Saeid Hosseini, Mojtaba Amiri Maskouni, Mohammadreza Kangavari i Ngai-Man Cheung. "Statistical Discretization of Continuous Attributes Using Kolmogorov-Smirnov Test". W Lecture Notes in Computer Science, 309–15. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92013-9_25.

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Nguyen, Hien D. "A Two-Sample Kolmogorov-Smirnov-Like Test for Big Data". W Communications in Computer and Information Science, 89–106. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0292-3_6.

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Streszczenia konferencji na temat "Kolmogorov-Smirnov test"

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Lopes, Raul. "A two-dimensional Kolmogorov-Smirnov test". W XI International Workshop on Advanced Computing and Analysis Techniques in Physics Research. Trieste, Italy: Sissa Medialab, 2009. http://dx.doi.org/10.22323/1.050.0045.

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Azim, Ali Waqar, Syed Safwan Khalid i Shafayat Abrar. "Modulation classification based on modified Kolmogorov-Smirnov test". W 2013 International Conference on Emerging Technologies (ICET). IEEE, 2013. http://dx.doi.org/10.1109/icet.2013.6743508.

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Lall, Ashwin. "Data streaming algorithms for the Kolmogorov-Smirnov test". W 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363746.

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Wang, Chengdong, Bo Zeng i Jiye Shao. "Application of bootstrap method in Kolmogorov-Smirnov test". W 2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE). IEEE, 2011. http://dx.doi.org/10.1109/icqr2mse.2011.5976614.

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Ahn, Seongjin, Jaeyoon Lee, Dongweon Yoon i Jun Won Choi. "Enhanced modulation classification algorithm based on Kolmogorov-Smirnov test". W 2017 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2017. http://dx.doi.org/10.1109/ictc.2017.8190976.

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Ceffer, Attila, i Janos Levendovszky. "Kolmogorov-Smirnov test for keystroke dynamics based user authentication". W 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2016. http://dx.doi.org/10.1109/cinti.2016.7846387.

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Choudhary, Divya, i Aaron Robinson. "Model selection and Kolmogorov-Smirnov test for ultrawideband channel modeling". W Defense and Security Symposium, redaktorzy Raghuveer M. Rao, Sohail A. Dianat i Michael D. Zoltowski. SPIE, 2007. http://dx.doi.org/10.1117/12.719553.

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dos Reis, Denis Moreira, Peter Flach, Stan Matwin i Gustavo Batista. "Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test". W KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939836.

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Köhler, Alexander, Ashkan Rigi i Michael Breuß. "Fast Shape Classification Using Kolmogorov-Smirnov Statistics". W WSCG'2022 - 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2022. Západočeská univerzita, 2022. http://dx.doi.org/10.24132/csrn.3201.22.

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Streszczenie:
The fast classification of shapes is an important problem in shape analysis and of high relevance for many possible applications. In this paper, we consider the use of very fast and easy to compute statistical techniques for assessing shapes, which may for instance be useful for a first similarity search in a shape database. To this end, we con- struct shape signatures at hand of stochastic sampling of distances between points of interest in a given shape. By employing the Kolmogorov-Smirnov statistics we then propose to formulate the problem of shape classification as a statistical hypothesis test that enables to assess the similarity of the signature distributions. In order to illus- trate some important properties of our approach, we explore the use of simple sampling techniques. At hand of experiments conducted with a variety of shapes in two dimensions, we give a discussion of potentially interesting features of the method.
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Okamura, Hiroyuki, i Tadashi Dohi. "On Kolmogorov-Smirnov Test for Software Reliability Models with Grouped Data". W 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS). IEEE, 2019. http://dx.doi.org/10.1109/qrs.2019.00023.

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Raporty organizacyjne na temat "Kolmogorov-Smirnov test"

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Meyer, Jack, i Robert Rasche. Kolmogorov-Smirnov Tests For Distribution Function Similarity With Applications To Portfolios of Common Stock. Cambridge, MA: National Bureau of Economic Research, marzec 1989. http://dx.doi.org/10.3386/t0076.

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