Academic literature on the topic 'Statistical hypotheses testing'

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Journal articles on the topic "Statistical hypotheses testing"

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Lehmann, E. L. "Testing Statistical Hypotheses." Biometrics 53, no. 4 (December 1997): 1563. http://dx.doi.org/10.2307/2533531.

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Neath, Andrew A. "Testing Statistical Hypotheses." Journal of the American Statistical Association 101, no. 474 (June 1, 2006): 847–48. http://dx.doi.org/10.1198/jasa.2006.s100.

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Ziegel, Eric. "Testing Statistical Hypotheses." Technometrics 29, no. 4 (November 1987): 494. http://dx.doi.org/10.1080/00401706.1987.10488294.

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Mukerjee, Hari, and E. L. Lehmann. "Testing Statistical Hypotheses." Journal of the American Statistical Association 82, no. 400 (December 1987): 1192. http://dx.doi.org/10.2307/2289421.

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Biggins, J. D., and E. L. Lehmann. "Testing Statistical Hypotheses." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 1 (1988): 231. http://dx.doi.org/10.2307/2982206.

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K. Sharma, Narendra. "Hypothesis Statement and Statistical Testing: A Tutorial." BOHR International Journal of Operations Management Research and Practices 1, no. 1 (2022): 52–58. http://dx.doi.org/10.54646/bijomrp.007.

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Many researchers and beginners in social research have several dilemmas and confusion in their mind about hypothesis statement and statistical testing of hypotheses. A distinction between the research hypothesis and statistical hypotheses, and understanding the limitations of the historically used null hypothesis statistical testing, is useful in clarifying these doubts. This article presents some data from the published research articles to support the view that the is format as well as the will format is appropriate to stating hypotheses. The article presents a social research framework to present the research hypothesis and statistical hypotheses is proper perspective.
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Lazar, Nicole. "Testing Statistical Hypotheses of Equivalence." Technometrics 45, no. 3 (August 2003): 271–72. http://dx.doi.org/10.1198/tech.2003.s775.

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Munk, Axel. "Testing Statistical Hypotheses of Equivalence." Journal of the American Statistical Association 99, no. 465 (March 2004): 293. http://dx.doi.org/10.1198/jasa.2004.s317.

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Meeden, Glen, W. C. M. Kallenberg, J. Beirlant, P. Van Blokland, J. J. Dik, P. J. M. M. Does, A. J. Van Es, et al. "Testing Statistical Hypotheses: Worked Solutions." Journal of the American Statistical Association 81, no. 395 (September 1986): 860. http://dx.doi.org/10.2307/2289027.

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COUSENS, ROGER, and CHARLES MARSHALL. "Dangers in testing statistical hypotheses." Annals of Applied Biology 111, no. 2 (October 1987): 469–76. http://dx.doi.org/10.1111/j.1744-7348.1987.tb01476.x.

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Dissertations / Theses on the topic "Statistical hypotheses testing"

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Cho, Jin Seo. "Three essays on testing hypotheses with irregular conditions /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3071015.

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Zhang, Jian. "Bayesian multiple hypotheses testing with quadratic criterion." Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0016/document.

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Le problème de détection et localisation d’anomalie peut être traité comme le problème du test entre des hypothèses multiples (THM) dans le cadre bayésien. Le test bayésien avec la fonction de perte 0−1 est une solution standard pour ce problème, mais les hypothèses alternatives pourraient avoir une importance tout à fait différente en pratique. La fonction de perte 0−1 ne reflète pas cette réalité tandis que la fonction de perte quadratique est plus appropriée. L’objectif de cette thèse est la conception d’un test bayésien avec la fonction de perte quadratique ainsi que son étude asymptotique. La construction de ce test est effectuée en deux étapes. Dans la première étape, un test bayésien avec la fonction de perte quadratique pour le problème du THM sans l’hypothèse de base est conçu et les bornes inférieures et supérieures des probabilités de classification erronée sont calculées. La deuxième étape construit un test bayésien pour le problème du THM avec l’hypothèse de base. Les bornes inférieures et supérieures des probabilités de fausse alarme, des probabilités de détection manquée, et des probabilités de classification erronée sont calculées. A partir de ces bornes, l’équivalence asymptotique entre le test proposé et le test standard avec la fonction de perte 0−1 est étudiée. Beaucoup d’expériences de simulation et une expérimentation acoustique ont illustré l’efficacité du nouveau test statistique
The anomaly detection and localization problem can be treated as a multiple hypotheses testing (MHT) problem in the Bayesian framework. The Bayesian test with the 0−1 loss function is a standard solution for this problem, but the alternative hypotheses have quite different importance in practice. The 0−1 loss function does not reflect this fact while the quadratic loss function is more appropriate. The objective of the thesis is the design of a Bayesian test with the quadratic loss function and its asymptotic study. The construction of the test is made in two steps. In the first step, a Bayesian test with the quadratic loss function for the MHT problem without the null hypothesis is designed and the lower and upper bounds of the misclassification probabilities are calculated. The second step constructs a Bayesian test for the MHT problem with the null hypothesis. The lower and upper bounds of the false alarm probabilities, the missed detection probabilities as well as the misclassification probabilities are calculated. From these bounds, the asymptotic equivalence between the proposed test and the standard one with the 0-1 loss function is studied. A lot of simulation and an acoustic experiment have illustrated the effectiveness of the new statistical test
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Fox, Gerardus Johannes Andre. "Multilevel IRT: a Bayesian perspective on estimating parameters and testing statistical hypotheses." Enschede : University of Twente [Host], 2001. http://doc.utwente.nl/58675.

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Seid, Hamid Jemila. "New residuals in multivariate bilinear models : testing hypotheses, diagnosing models and validating model assumptions /." Uppsala : Dept. of Biometry and Engineering, Swedish University of Agricultural Sciences, 2005. http://epsilon.slu.se/200583.pdf.

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An, Qian. "A Monte Carlo study of several alpha-adjustment procedures using a testing multiple hypotheses in factorial anova." Ohio : Ohio University, 2010. http://www.ohiolink.edu/etd/view.cgi?ohiou1269439475.

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Occhipinti, Rossana. "In Silico Testing of Hypotheses for Brain Energy Metabolism with New Computational Models within a Statistical Framework." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1244144388.

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An, Qian. "A Monte Carlo Study of Several Alpha-Adjustment Procedures Used in Testing Multiple Hypotheses in Factorial Anova." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1269439475.

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Hardy, James C. (James Clifford). "A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form." Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc332130/.

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When selecting a method for testing variance equality, a researcher should select a method which is robust to distribution non-normality and dissimilarity. The method should also possess sufficient power to ascertain departures from the equal variance hypothesis. This Monte Carlo study examined the robustness and power of five tests of variance equality under specific conditions. The tests examined included one procedure proposed by O'Brien (1978), two by O'Brien (1979), and two by Conover, Johnson, and Johnson (1981). Specific conditions included assorted combinations of the following factors: k=2 and k=3 groups, normal and non-normal distributional forms, similar and dissimilar distributional forms, and equal and unequal sample sizes. Under the k=2 group condition, a total of 180 combinations were examined. A total of 54 combinations were examined under the k=3 group condition. The Type I error rates and statistical power estimates were based upon 1000 replications in each combination examined. Results of this study suggest that when sample sizes are relatively large, all five procedures are robust to distribution non-normality and dissimilarity, as well as being sufficiently powerful.
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Jeng, Tian-Tzer. "Some contributions to asymptotic theory on hypothesis testing when the model is misspecified /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487332636473942.

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Williams, Elliot. "Essays in multiple comparison testing /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3112194.

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Books on the topic "Statistical hypotheses testing"

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Lehmann, E. L. Testing statistical hypotheses. 2nd ed. New York: Springer, 1986.

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Lehmann, E. L. Testing statistical hypotheses. 2nd ed. New York: Springer, 1997.

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Lehmann, E. L., and Joseph P. Romano. Testing Statistical Hypotheses. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-70578-7.

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Lehmann, E. L. Testing Statistical Hypotheses. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4757-1923-9.

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1960-, Romano Joseph P., ed. Testing statistical hypotheses. 3rd ed. New York: Springer, 2005.

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Testing statistical hypotheses. 2nd ed. Pacific Grove, Calif: Wadsworth & Brooks/Cole Advanced Books & Software, 1991.

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Testing statistical hypotheses. 2nd ed. New York: Wiley, 1986.

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Testing statistical hypotheses. 2nd ed. New York: Springer, 1997.

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Testing statistical hypotheses of equivalence. Boca Raton, FL: Chapman & Hall/CRC, 2003.

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Testing statistical hypotheses of equivalence and noninferiority. 2nd ed. Boca Raton: CRC Press, 2010.

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Book chapters on the topic "Statistical hypotheses testing"

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Brandt, Siegmund. "Testing Statistical Hypotheses." In Data Analysis, 175–207. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03762-2_8.

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Andersen, Erling B., Niels-Erik Jensen, and Nils Kousgaard. "Testing Statistical Hypotheses." In Statistics for Economics, Business Administration, and the Social Sciences, 214–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-95528-0_12.

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Olive, David J. "Testing Statistical Hypotheses." In Statistical Theory and Inference, 183–213. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04972-4_7.

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Vidakovic, Brani. "Testing Statistical Hypotheses." In Springer Texts in Statistics, 317–54. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0394-4_9.

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Brandt, Siegmund. "Testing Statistical Hypotheses." In Data Analysis, 212–47. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1446-5_8.

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Kutoyants, Yury A. "Hypotheses Testing." In Statistical Inference for Ergodic Diffusion Processes, 421–60. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3866-2_6.

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Young, Linda J., and Jerry H. Young. "Sequentially Testing Three Hypotheses." In Statistical Ecology, 191–214. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2829-3_6.

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Bedbur, Stefan, and Udo Kamps. "Hypotheses Testing." In Multivariate Exponential Families: A Concise Guide to Statistical Inference, 93–115. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81900-2_5.

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Holický, Milan. "Testing of Statistical Hypotheses." In Introduction to Probability and Statistics for Engineers, 125–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38300-7_10.

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Dickhaus, Thorsten. "Software Solutions for Multiple Hypotheses Testing." In Simultaneous Statistical Inference, 117–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45182-9_8.

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Conference papers on the topic "Statistical hypotheses testing"

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Ryabko, Daniil. "Testing statistical hypotheses about ergodic processes." In 2008 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering (SIBIRCON). IEEE, 2008. http://dx.doi.org/10.1109/sibircon.2008.4602643.

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Jamkhaneh, Ezzatallah Baloui, and Asgar Nadi Ghara. "Testing Statistical Hypotheses with Fuzzy Data." In 2010 International Conference on Intelligent Computing and Cognitive Informatics (ICICCI). IEEE, 2010. http://dx.doi.org/10.1109/icicci.2010.56.

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Ermis, Erhan Baki, and Venkatesh Saligrama. "Dynamic Thresholding for Distributed Multiple Hypotheses Testing." In 2007 IEEE/SP 14th Workshop on Statistical Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/ssp.2007.4301344.

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ICHIM, D., I. PERONI, and F. SPARASCI. "STATISTICAL HYPOTHESES TESTING FOR PHASE TRANSITION IDENTIFICATION IN CRYOGENIC THERMOMETRY." In Advanced Mathematical and Computational Tools in Metrology. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702647_0018.

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Palukha, Uladzimir, and Yuriy Kharin. "Statistical hypotheses testing for random and pseudorandom generators based on statistical estimators of entropy." In 2017 International Conference on Information and Digital Technologies (IDT). IEEE, 2017. http://dx.doi.org/10.1109/dt.2017.8024311.

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Zheglov, Sergey, and Andrei Akhremenko. "Testing Hypotheses on the Street Protest Efficiency Factors: Combined Statistical Strategy." In 2020 13th International Conference Management of large-scale system development (MLSD). IEEE, 2020. http://dx.doi.org/10.1109/mlsd49919.2020.9247750.

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Denezhkina, Irina, and Sergey Zadadaev. "Testing statistical hypotheses with the use of visualization tools in RStudio." In System analysis in economics – 2018. Prometheus publishing house, 2018. http://dx.doi.org/10.33278/sae-2018.eng.150-152.

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Konold, Clifford. "Understandin Probability and Statistical Inference through Resampling." In Proceedings of the First Scientific Meeting of the IASE. International Association for Statistical Education, 1993. http://dx.doi.org/10.52041/srap.93304.

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During the past four years, I have been developing curricula and computer software for teaching probability and data analysis at the introductory high-school and college level. The approach I've taken emphasizes the use of real data, where "telling a story" takes priority over testing hypotheses, and in which mathematical formalism is kept to a minimum (see Cobb, 1992; Scheaffer, 1990; Watkins et al. 1992).
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Vaiciulyte, Ingrida. "Adaptive Monte-Carlo Markov chain for multivariate statistical estimation." In International Workshop of "Stochastic Programming for Implementation and Advanced Applications". The Association of Lithuanian Serials, 2012. http://dx.doi.org/10.5200/stoprog.2012.21.

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The estimation of the multivariate skew t-distribution by the Monte-Carlo Markov Chain (MCMC) method is considered in the paper. Thus, the MCMC procedure is constructed for recurrent estimation of skew t-distribution, following the maximum likelihood method, where the Monte-Carlo sample size is regulated to ensure the convergence and to decrease the total amount of Monte-Carlo trials, required for estimation. The confidence intervals of Monte-Carlo estimators are introduced because of their asymptotic normality. The termination rule is also implemented by testing statistical hypotheses on an insignificant change of estimates in two steps of the procedure. The algorithm developed has been tested by computer simulation with test example. The test sample, following from skew t-distribution, has been simulated by computer and parameters of the skew t-distribution have been estimated by MathCAD. Next, the chi-squared criterion confirmed the hypothesis of distribution of statistics with respect to under- lying distribution function. Thus, computer simulation confirmed the applicability of the Monte-Carlo Markov chain approach with adaptively regulated sample size for estimation of parameters of the skew t- distribution with acceptable accuracy.
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Nikitin, Stanislav V., Andrey Medvedev, Alexander S. Emilenko, Oleg V. Postylyakov, and Alexey I. Chulichkov. "Selection of optical model of stereophotography experiment for determination the cloud base height as a problem of testing of statistical hypotheses." In Remote Sensing of Clouds and the Atmosphere, edited by Adolfo Comerón, Evgueni I. Kassianov, and Klaus Schäfer. SPIE, 2017. http://dx.doi.org/10.1117/12.2279553.

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Reports on the topic "Statistical hypotheses testing"

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Dolor, Jason Mark. Investigating Statistics Teachers' Knowledge of Probability in the Context of Hypothesis Testing. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5914.

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Panchenko, Liubov, and Andrii Khomiak. Education Statistics: Looking for Case-Study for Modeling. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4461.

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The article deals with the problem of using modeling in social statistics courses. It allows the student-researcher to build one-dimensional and multidimensional models of the phenomena and processes that are being studied. Social Statistics course programs from foreign universities (University of Arkansas; Athabasca University; HSE University, Russia; McMaster University, Canada) are analyzed. The article provides an example using the education data set – Guardian UK universities ranking in Social Statistics course. Examples of research questions are given, data analysis for these questions is performed (correlation, hypothesis testing, discriminant analysis). During the research the discriminant model with group variable – modified Guardian score – and 9 predictors: course satisfaction, teaching quality, feedback, staff-student ratio, money spent on each student and other) was built. Lower student’s satisfaction with feedback was found to be significantly different from the satisfaction with teaching. The article notes the modeling and statistical analysis should be accompanied by a meaningful interpretation of the results. In this example, we discussed the essence of university ratings, the purpose of Guardian rating, the operationalization and measurement of such concepts as satisfaction with teaching, feedback; ways to use statistics in education, data sources etc. with students. Ways of using this education data in group and individual work of students are suggested.
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Nuttall, Albert H., and Paul M. Baggenstoss. Joint Distributions for Two Useful Classes of Statistics, With Applications to Classification and Hypothesis Testing. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada477141.

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Raj, Sunny, Sumit Kumar Jha, Laura L. Pullum, and Arvind Ramanathan. Statistical Hypothesis Testing using CNN Features for Synthesis of Adversarial Counterexamples to Human and Object Detection Vision Systems. Office of Scientific and Technical Information (OSTI), May 2017. http://dx.doi.org/10.2172/1361358.

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Amengual, Dante, Xinyue Bei, Marine Carrasco, and Enrique Sentana. Score-type tests for normal mixtures. CIRANO, January 2023. http://dx.doi.org/10.54932/uxsg1990.

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Testing normality against discrete normal mixtures is complex because some parameters turn increasingly underidentified along alternative ways of approaching the null, others are inequality constrained, and several higher-order derivatives become identically 0. These problems make the maximum of the alternative model log-likelihood function numerically unreliable. We propose score-type tests asymptotically equivalent to the likelihood ratio as the largest of two simple intuitive statistics that only require estimation under the null. One novelty of our approach is that we treat symmetrically both ways of writing the null hypothesis without excluding any region of the parameter space. We derive the asymptotic distribution of our tests under the null and sequences of local alternatives. We also show that their asymptotic distribution is the same whether applied to observations or standardized residuals from heteroskedastic regression models. Finally, we study their power in simulations and apply them to the residuals of Mincer earnings functions.
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