Relevant bibliographies by topics / Goodness-of-fit tests

Academic literature on the topic 'Goodness-of-fit tests'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Goodness-of-fit tests"

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Arrasmith, A., B. Follin, E. Anderes, and L. Knox. "Tuning goodness-of-fit tests†." Monthly Notices of the Royal Astronomical Society 484, no. 2 (January 9, 2019): 1889–98. http://dx.doi.org/10.1093/mnras/stz066.

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Frey, Jesse. "Unbiased goodness-of-fit tests." Journal of Statistical Planning and Inference 139, no. 10 (October 2009): 3690–97. http://dx.doi.org/10.1016/j.jspi.2009.04.017.

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Kemp, A. W., J. C. W. Rayner, and D. J. Best. "Smooth Tests of Goodness of Fit." Biometrics 47, no. 2 (June 1991): 788. http://dx.doi.org/10.2307/2532179.

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King, Terry. "Smooth Tests of Goodness of Fit." Technometrics 33, no. 4 (November 1991): 491. http://dx.doi.org/10.1080/00401706.1991.10484896.

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Rayner, J. C. W., O. Thas, and D. J. Best. "Smooth tests of goodness of fit." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 5 (April 15, 2011): 397–406. http://dx.doi.org/10.1002/wics.171.

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Shapiro, Alexander, Yao Xie, and Rui Zhang. "Goodness-of-Fit Tests on Manifolds." IEEE Transactions on Information Theory 67, no. 4 (April 2021): 2539–53. http://dx.doi.org/10.1109/tit.2021.3050469.

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Hora, Stephen C. "Goodness of fit tests using regression." Communications in Statistics - Theory and Methods 14, no. 2 (January 1985): 307–32. http://dx.doi.org/10.1080/03610928508828914.

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Fermanian, Jean-David. "Goodness-of-fit tests for copulas." Journal of Multivariate Analysis 95, no. 1 (July 2005): 119–52. http://dx.doi.org/10.1016/j.jmva.2004.07.004.

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RESCHENHOFER, E., and I. M. BOMZE. "Length tests for goodness of fit." Biometrika 78, no. 1 (1991): 207–16. http://dx.doi.org/10.1093/biomet/78.1.207.

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Barron, Andrew R. "Uniformly Powerful Goodness of Fit Tests." Annals of Statistics 17, no. 1 (March 1989): 107–24. http://dx.doi.org/10.1214/aos/1176347005.

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Dissertations / Theses on the topic "Goodness-of-fit tests"

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Liero, Hannelore. "Goodness of Fit Tests of L2-Type." Universität Potsdam, 2003. http://opus.kobv.de/ubp/volltexte/2011/5149/.

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We give a survey on procedures for testing functions which are based on quadratic deviation measures. The following problems are considered: Testing whether a density function lies in a parametric class of functions, whether continuous random variables are independent; testing cell probabilities and independence in sparse data sets; testing the parametric fit of a regression homoscedasticity in a regression model and testing the hazard rate in survival models with censoring and with and without covariates.
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Hallett, David C. "Goodness of fit tests in logistic regression." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq45403.pdf.

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Zhang, Jin. "Powerful goodness-of-fit and multisample tests." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ66371.pdf.

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Eren, Emrah. "Effect Of Estimation In Goodness-of-fit Tests." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12611046/index.pdf.

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In statistical analysis, distributional assumptions are needed to apply parametric procedures. Assumptions about underlying distribution should be true for accurate statistical inferences. Goodness-of-fit tests are used for checking the validity of the distributional assumptions. To apply some of the goodness-of-fit tests, the unknown population parameters are estimated. The null distributions of test statistics become complicated or depend on the unknown parameters if population parameters are replaced by their estimators. This will restrict the use of the test. Goodness-of-fit statistics which are invariant to parameters can be used if the distribution under null hypothesis is a location-scale distribution. For location and scale invariant goodness-of-fit tests, there is no need to estimate the unknown population parameters. However, approximations are used in some of those tests. Different types of estimation and approximation techniques are used in this study to compute goodness-of-fit statistics for complete and censored samples from univariate distributions as well as complete samples from bivariate normal distribution. Simulated power properties of the goodness-of-fit tests against a broad range of skew and symmetric alternative distributions are examined to identify the estimation effects in goodness-of-fit tests. The main aim of this thesis is to modify goodness-of-fit tests by using different estimators or approximation techniques, and finally see the effect of estimation on the power of these tests.
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Liu, Tianyi. "Power Comparison of Some Goodness-of-fit Tests." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2572.

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There are some existing commonly used goodness-of-fit tests, such as the Kolmogorov-Smirnov test, the Cramer-Von Mises test, and the Anderson-Darling test. In addition, a new goodness-of-fit test named G test was proposed by Chen and Ye (2009). The purpose of this thesis is to compare the performance of some goodness-of-fit tests by comparing their power. A goodness-of-fit test is usually used when judging whether or not the underlying population distribution differs from a specific distribution. This research focus on testing whether the underlying population distribution is an exponential distribution. To conduct statistical simulation, SAS/IML is used in this research. Some alternative distributions such as the triangle distribution, V-shaped triangle distribution are used. By applying Monte Carlo simulation, it can be concluded that the performance of the Kolmogorov-Smirnov test is better than the G test in many cases, while the G test performs well in some cases.
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Cigsar, Candemir. "Goodness-of-fit Tests Based On Censored Samples." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606226/index.pdf.

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In this study, the most prominent goodness-of-fit tests for censored samples are reviewed. Power properties of goodness-of-fit statistics of the null hypothesis that a sample which is censored from right, left and both right and left which comes from uniform, normal and exponential distributions are investigated. Then, by a similar argument extreme value, student t with 6 degrees of freedom and generalized logistic distributions are discussed in detail through a comprehensive simulation study. A variety of real life applications are given. Suitable test statistics for testing the above distributions for censored samples are also suggested in the conclusion.
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Lü, Wei, and 吕薇. "On some goodness-of-fit tests for copulas." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B47849964.

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Copulas have been known in the statistical literature for many years, and have become useful tools in modeling dependence structure of multivariate random variables, overcoming some of the drawbacks of the commonly-used correlation measures. Goodness-of-fit tests for copulas play a very important role in evaluating the suitability of a potential input copula model. In recent years, many approaches have been proposed for constructing goodness-of-fit tests for copula families. Among them, the so-called “blanket tests" do not require an arbitrary data categorization or any strategic choice of weight function, smoothing parameter, kernel, and so on. As preliminaries, some background and related results of copulas are firstly presented. Three goodness-of-fit test statistics belonging to the blanket test classification are then introduced. Since the asymptotic distributions of the test statistics are very complicated, parametric bootstrap procedures are employed to approximate critical values of the test statistics under the null hypotheses. To assess the performance of the three test statistics in the low dependence cases, simulation studies are carried out for three bivariate copula families, namely the Gumbel-Hougaard copula family, the Ali-Mikhail-Haq copula family, and the Farlie-Gumbel-Morgenstern copula family. Specifically the effect of low dependence on the empirical sizes and powers of the three blanket tests under various combinations of null and alternative copula families are examined. Furthermore, to check the performance of the three tests for higher dimensional copulas, the simulation studies are extended to some three-dimensional copulas. Finally the three goodness-of-fit tests are applied to two real data sets.
published_or_final_version
Statistics and Actuarial Science
Master
Master of Philosophy
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Kpanzou, Tchilabalo Abozou. "Aspects of copulas and goodness-of-fit." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019/1949.

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Steele, Michael C., and 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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Books on the topic "Goodness-of-fit tests"

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Révész, Pál, K. Sarkadi, and Pranab Kumar Sen, eds. Goodness-of-fit. Amsterdam, Netherlands: North-Holland Pub. Co, 1987.

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J, Best D., and Thas O, eds. Smooth tests of goodness of fit. 2nd ed. Hoboken, NJ: Wiley, 2009.

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Rayner, J. C. W. Smooth tests of goodness of fit. New York: Oxford University Press, 1989.

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Fotheringham, A. Stewart. Goodness-of-fit statistics. Norwich: Geo Books, 1987.

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N, Balakrishnan, Nikulin M. S, and Mesbah M, eds. Goodness-of-Fit Tests and Model Validity. Boston, MA: Birkhäuser Boston, 2002.

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Koning, A. J. Stochastic integrals and goodness-of-fit tests. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, 1993.

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Hallett, David C. Goodness of fit tests in logistic regression. Ottawa: National Library of Canada, 1999.

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Huber-Carol, C., N. Balakrishnan, M. S. Nikulin, and M. Mesbah, eds. Goodness-of-Fit Tests and Model Validity. Boston, MA: Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-1-4612-0103-8.

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Weichselberger, Andreas. Ein neuer nichtparametrischer Anpassungstest zur Beurteilung der Lage von Verteilungen. Göttingen: Vandenhoeck & Ruprecht, 1993.

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W, Evans James. Two- and three-parameter Weibull goodness-of-fit tests. [Madison, Wis.]: U.S Dept. of Agriculture, Forest Service, Forest Products Laboratory, 1989.

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Book chapters on the topic "Goodness-of-fit tests"

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Dickhaus, Thorsten. "Goodness-of-Fit Tests." In Theory of Nonparametric Tests, 37–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76315-6_3.

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Young, Linda J., and Jerry H. Young. "Goodness-of-Fit Tests." In Statistical Ecology, 42–74. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2829-3_2.

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Mielke, Paul W., and Kenneth J. Berry. "Goodness-of-Fit Tests." In Springer Series in Statistics, 239–55. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3449-2_6.

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Vidakovic, Brani. "Goodness-of-Fit Tests." In Springer Texts in Statistics, 503–30. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0394-4_13.

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Mielke, Paul W., and Kenneth J. Berry. "Goodness-of-Fit Tests." In Springer Series in Statistics, 263–82. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-69813-7_6.

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Jolicoeur, Pierre. "Tests of goodness of fit." In Introduction to Biometry, 102–7. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4777-8_17.

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Gibbons, Jean Dickinson, and Subhabrata Chakraborti. "Tests of Goodness of Fit." In Nonparametric Statistical Inference, 107–66. 6th edition. | Boca Raton : CRC Press, 2021.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315110479-4.

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Rees, D. G. "χ2 goodness-of-fit tests." In Essential Statistics, 167–80. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-7260-6_15.

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Alvo, Mayer, and Philip L. H. Yu. "Smooth Goodness of Fit Tests." In A Parametric Approach to Nonparametric Statistics, 63–89. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94153-0_4.

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Bosq, Denis. "Functional Tests of Fit." In Goodness-of-Fit Tests and Model Validity, 341–56. Boston, MA: Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-1-4612-0103-8_25.

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Conference papers on the topic "Goodness-of-fit tests"

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Rakonczai, Pál, and András Zempléni. "Copulas and goodness of fit tests." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0024.

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Lei, Shaoting, Haiquan Wang, and Lei Shen. "Spectrum sensing based on goodness of fit tests." In 2011 International Conference on Electronics, Communications and Control (ICECC). IEEE, 2011. http://dx.doi.org/10.1109/icecc.2011.6067691.

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Kundargi, Nikhil, and Ahmed Tewfik. "Inference using phi-divergence Goodness-of-Fit tests." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288546.

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Karadağ, Özge, and Serpil Aktaş. "Goodness of fit tests for generalized gamma distribution." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). Author(s), 2016. http://dx.doi.org/10.1063/1.4952355.

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Unnikrishnan, Jayakrishnan, Sean Meyn, and Venugopal V. Veeravalli. "On thresholds for robust goodness-of-fit tests." In 2010 IEEE Information Theory Workshop (ITW 2010). IEEE, 2010. http://dx.doi.org/10.1109/cig.2010.5592803.

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Lemeshko, B. Y., and A. P. Rogozhnikov. "Simulation study of some goodness-or-fit tests properties." In 2008 9th International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE). IEEE, 2008. http://dx.doi.org/10.1109/apeie.2008.4897176.

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Szynal, Dominik. "Goodness-of-fit tests derived from characterizations of continuous distributions." In Stability in Probability. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2010. http://dx.doi.org/10.4064/bc90-0-14.

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ur Rehman, Naveed, Khuram Naveed, Shoaib Ehsan, and Klaus McDonald-Maier. "Multi-scale image denoising based on goodness of fit (GOF) tests." In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760508.

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Gaskin, Joseph, Galib Abumeri, and Christos Chamis. "Goodness of fit tests at all percentile levels for probabilistic simulation methods." In 19th AIAA Applied Aerodynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-1652.

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Lemeshko, S. B. "Distribution of Statistics of Chi-Square Goodness-of-Fit Tests for Small Samples." In 2006 8th International Conference on Actual Problems of Electronic Instrument Engineering. IEEE, 2006. http://dx.doi.org/10.1109/apeie.2006.4292559.

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Reports on the topic "Goodness-of-fit tests"

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Parzen, Emanuel. Goodness of Fit Tests and Entropy. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada224860.

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McKeague, Ian W., and Klaus J. Utikal. Goodness-of-Fit Tests for Additive Hazards and Proportional Hazards Models. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada202440.

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Parker, Thomas. A comparison of alternative approaches to sup-norm goodness of fit tests with estimated parameters. Institute for Fiscal Studies, November 2010. http://dx.doi.org/10.1920/wp.cem.2010.3410.

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Dickens, William, and Kevin Lang. A Goodness of Fit Test of Dual Labor Market Theory. Cambridge, MA: National Bureau of Economic Research, August 1987. http://dx.doi.org/10.3386/w2350.

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Kedem, Benjamin. A Fast Graphical Goodness of Fit Test for Time Series Models. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada170094.

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Conover, W. J., D. D. Cox, and H. F. Martz. A chi-square goodness-of-fit test for non-identically distributed random variables: with application to empirical Bayes. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/645488.

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