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Auswahl der wissenschaftlichen Literatur zum Thema „Proportion inference“
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Zeitschriftenartikel zum Thema "Proportion inference"
Xu, XingZhong, und Fang Liu. „Statistical inference on mixing proportion“. Science in China Series A: Mathematics 51, Nr. 9 (13.06.2008): 1593–608. http://dx.doi.org/10.1007/s11425-008-0016-0.
Der volle Inhalt der QuelleSubbiah, M., und V. Rajeswaran. „proportion: A comprehensive R package for inference on single Binomial proportion and Bayesian computations“. SoftwareX 6 (2017): 36–41. http://dx.doi.org/10.1016/j.softx.2017.01.001.
Der volle Inhalt der QuelleMielke, Paul W., und Kenneth J. Berry. „Nonasymptotic Inferences Based on Cochran's Q Test“. Perceptual and Motor Skills 81, Nr. 1 (August 1995): 319–22. http://dx.doi.org/10.2466/pms.1995.81.1.319.
Der volle Inhalt der QuelleGarthwaite, Paul H., und John R. Crawford. „Inference for a binomial proportion in the presence of ties“. Journal of Applied Statistics 38, Nr. 9 (September 2011): 1915–34. http://dx.doi.org/10.1080/02664763.2010.537649.
Der volle Inhalt der QuelleMohamed, Nuri Eltabit. „Approximate confidence intervals for the Population Proportion based on linear model“. Al-Mukhtar Journal of Basic Sciences 21, Nr. 2 (05.05.2024): 58–62. http://dx.doi.org/10.54172/rtn9cg93.
Der volle Inhalt der QuelleFrey, Jesse. „Bayesian Inference on a Proportion Believed to be a Simple Fraction“. American Statistician 61, Nr. 3 (August 2007): 201–6. http://dx.doi.org/10.1198/000313007x222866.
Der volle Inhalt der QuelleNandram, Balgobin, Dilli Bhatta, Dhiman Bhadra und Gang Shen. „Bayesian predictive inference of a finite population proportion under selection bias“. Statistical Methodology 11 (März 2013): 1–21. http://dx.doi.org/10.1016/j.stamet.2012.08.003.
Der volle Inhalt der QuelleKhan, K. Daniel, und J. A. A. Vargas-Guzmán. „Facies Proportions From the Inference of Nonlinear Conditional Beta-Field Parameters“. SPE Journal 18, Nr. 06 (06.05.2013): 1033–42. http://dx.doi.org/10.2118/163147-pa.
Der volle Inhalt der QuelleHilbig, Benjamin E., und Rüdiger F. Pohl. „Recognizing Users of the Recognition Heuristic“. Experimental Psychology 55, Nr. 6 (Januar 2008): 394–401. http://dx.doi.org/10.1027/1618-3169.55.6.394.
Der volle Inhalt der QuelleRahardja, Dewi, und Yan D. Zhao. „Bayesian inference of a binomial proportion using one-sample misclassified binary data“. Model Assisted Statistics and Applications 7, Nr. 1 (16.02.2012): 17–22. http://dx.doi.org/10.3233/mas-2011-0197.
Der volle Inhalt der QuelleDissertationen zum Thema "Proportion inference"
Kim, Hyun Seok (John). „Diagnosing examinees' attributes-mastery using the Bayesian inference for binomial proportion: a new method for cognitive diagnostic assessment“. Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41144.
Der volle Inhalt der QuelleLi, Qiuju. „Statistical inference for joint modelling of longitudinal and survival data“. Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/statistical-inference-for-joint-modelling-of-longitudinal-and-survival-data(65e644f3-d26f-47c0-bbe1-a51d01ddc1b9).html.
Der volle Inhalt der QuelleZHAO, SHUHONG. „STATISTICAL INFERENCE ON BINOMIAL PROPORTIONS“. University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1115834351.
Der volle Inhalt der QuelleSimonnet, Titouan. „Apprentissage et réseaux de neurones en tomographie par diffraction de rayons X. Application à l'identification minéralogique“. Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1033.
Der volle Inhalt der QuelleUnderstanding the chemical and mechanical behavior of compacted materials (e.g. soil, subsoil, engineered materials) requires a quantitative description of the material's structure, and in particular the nature of the various mineralogical phases and their spatial relationships. Natural materials, however, are composed of numerous small-sized minerals, frequently mixed on a small scale. Recent advances in synchrotron-based X-ray diffraction tomography (to be distinguished from phase contrast tomography) now make it possible to obtain tomographic volumes with nanometer-sized voxels, with a XRD pattern for each of these voxels (where phase contrast only gives a gray level). On the other hand, the sheer volume of data (typically on the order of 100~000 XRD patterns per sample slice), combined with the large number of phases present, makes quantitative processing virtually impossible without appropriate numerical codes. This thesis aims to fill this gap, using neural network approaches to identify and quantify minerals in a material. Training such models requires the construction of large-scale learning bases, which cannot be made up of experimental data alone.Algorithms capable of synthesizing XRD patterns to generate these bases have therefore been developed.The originality of this work also concerned the inference of proportions using neural networks. To meet this new and complex task, adapted loss functions were designed.The potential of neural networks was tested on data of increasing complexity: (i) from XRD patterns calculated from crystallographic information, (ii) using experimental powder XRD patterns measured in the laboratory, (iii) on data obtained by X-ray tomography. Different neural network architectures were also tested. While a convolutional neural network seemed to provide interesting results, the particular structure of the diffraction signal (which is not translation invariant) led to the use of models such as Transformers. The approach adopted in this thesis has demonstrated its ability to quantify mineral phases in a solid. For more complex data, such as tomography, improvements have been proposed
Liu, Guoyuan. „Comparison of prior distributions for bayesian inference for small proportions“. Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96917.
Der volle Inhalt der QuelleSouvent des analyses bayésiennes de données épidémiologiques utilisent les distributions à priori objectives. Ces distributions à priori sont sélectionnées de sorte que les distributions à posteriori soient déterminées uniquement par les données observées. Bien que cette méthode soit efficace dans plusieurs situations, elle ne l'est pas dans le cas de l'estimation bayésienne de petites proportions. Cette situation peut survenir, par exemple lors de l'estimation de la prévalence d'une maladie rare. Plusieurs distributions à priori objectives ont été proposées pour l'estimation d'une proportion, telle que, par exemple la distribution uniforme de Jeffrey. Chacune de ces distributions à priori peut conduire à de différentes distributions à posteriori lorsque le nombre d'événements dans l'expérience binomiale est petit. Mais il n'est pas clair laquelle de ces distributions, en moyenne, donne de meilleurs estimés. Nous explorons cette question en examinant la performance fréquentiste des intervalles crédibles à posteriori obtenus, respectivement, avec chacune de ces distributions à priori. Pour évaluer cette performance, nous considèrons des statistiques comme la couverture moyenne et la longueur moyenne des intervalles crédibles à posteriori. Nous considérons aussi des distributions à priori plus informatives comme les distributions uniformes définies sur un sous-intervalle de l'intervalle [0, 1]. La performance des distributions à priori est évaluée en utilisant des données simulées de situations où l'intérêt de recherche est concentré sur l'estimation d'une seule proportion ou sur la différence entre deux proportions.
LEAL, ALTURO Olivia Lizeth. „Nonnested hypothesis testing inference in regression models for rates and proportions“. Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/24573.
Der volle Inhalt der QuelleMade available in DSpace on 2018-05-07T21:17:28Z (GMT). No. of bitstreams: 1 DISSERTAÇÃO Olivia Lizeth Leal Alturo.pdf: 2450256 bytes, checksum: 8d29b676eaffcb3c5bc1b78a8611b9f8 (MD5) Previous issue date: 2017-02-16
Existem diferentes modelos de regressão que podem ser usados para modelar taxas, proporções e outras variáveis respostas que assumem valores no intervalo unitário padrão, (0,1). Quando só uma classe de modelos de regressão é considerada, a seleção do modelos pode ser baseada nos testes de hipóteses usuais. O objetivo da presente dissertação é apresentar e avaliar numericamente os desempenhos em amostras imitas de testes que podem ser usados quando há dois ou mais modelos que são plausíveis, são não-encaixados e pertencem a classes de modelos de regressão distintas. Os modelos competidores podem diferir nos regressores que utilizam, nas funções de ligação e/ou na distribuição assumida para a variável resposta. Através de simulações de Monte Cario nós estimamos as taxas de rejeição nulas e não-nulas dos testes sob diversos cenários. Avaliamos também o desempenho de um procedimento de seleção de modelos. Os resultados mostram que os testes podem ser bastante úteis na escolha do melhor modelo de regressão quando a variável resposta assume valores no intervalo unitário padrão.
There are several different regression models that can be used with rates, proportions and other continuous responses that assume values in the standard unit interval, (0,1). When only one class of models is considered, model selection can be based on standard hypothesis testing inference. In this dissertation, we develop tests that can be used when the practitioner has at his/her disposal more than one plausible model, the competing models are nonnested and possibly belong to different classes of models. The competing models can differ in the regressors they use, in the link functions and even in the response distribution. The finite sample performances of the proposed tests are numerically eval-uated. We evaluate both the null and nonnull behavior of the tests using Monte Cario simulations. The results show that the tests can be quite useful for selecting the best regression model when the response assumes values in the standard unit interval.
Ainsworth, Holly Fiona. „Bayesian inference for stochastic kinetic models using data on proportions of cell death“. Thesis, University of Newcastle upon Tyne, 2014. http://hdl.handle.net/10443/2499.
Der volle Inhalt der QuelleXiao, Yongling 1972. „Bootstrap-based inference for Cox's proportional hazards analyses of clustered censored survival data“. Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98523.
Der volle Inhalt der QuelleMethods. Both bootstrap-based approaches involve 2 stages of resampling the original data. The two methods share the same procedure at the first stage but employ different procedures at the second stage. At the first stage of both methods, the clusters (e.g. physicians) are resampled with replacement. At the second stage, one method resamples individual patients with replacement for each physician (i.e. units within-cluster) selected at the 1st stage, while another method picks up all the patients for each selected physician, without resampling. For both methods, each of the resulting bootstrap samples is then independently analyzed with standard Cox's PH model, and the standard errors (SE) of the regression parameters are estimated as the empirical standard deviation, of the corresponding estimates. Finally, 95% confidence intervals (CI) for the estimates are estimated using bootstrap-based SE and assuming normality.
Simulations design. I have simulated a hypothetical study with N patients clustered within practices of M physicians. Individual patients' times-to-events were generated from the exponential distribution with hazard conditional on (i) several patient-level variables, (ii) several cluster-level (physician's) variables, and (iii) physician's "random effects". Random right censoring was applied. Simulated data were analyzed using 4 approaches: the proposed two bootstrap methods, standard Cox's PH model and "classic" one-step bootstrap with direct resampling of the patients.
Results. Standard Cox's model and "Classic" 1-step bootstrap under-estimated variance of regression coefficients, leading to serious inflation of type I error rates and coverage rates of 95% CI as low as 60-70%. In contrast, the proposed approach that resamples both physicians and patients-within-physicians, with the 100 bootstrap resamples, resulted in slightly conservative estimates of standard errors, which yielded type I error rates between 2% and 6%, and coverage rates between 94% and 99%.
Conclusions. The proposed bootstrap approach offers an easy-to-implement method to account for interdependence of times-to-events in the inference about Cox model regression parameters in the context of analyses of right-censored clustered data.
Silva, Ana Roberta dos Santos 1989. „Modelos de regressão beta retangular heteroscedásticos aumentados em zeros e uns“. [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306787.
Der volle Inhalt der QuelleDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
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Resumo: Neste trabalho desenvolvemos a distribuição beta retangular aumentada em zero e um, bem como um correspondente modelo de regressão beta retangular aumentado em zero e um para analisar dados limitados-aumentados (representados por variáveis aleatórias mistas com suporte limitado), que apresentam valores discrepantes. Desenvolvemos ferramentas de inferência sob as abordagens bayesiana e frequentista. No que diz respeito à inferência bayesiana, devido à impossibilidade de obtenção analítica das posteriores de interesse, utilizou-se algoritmos MCMC. Com relação à estimação frequentista, utilizamos o algoritmo EM. Desenvolvemos técnicas de análise de resíduos, utilizando o resíduo quantil aleatorizado, tanto sob o enfoque frequentista quanto bayesiano. Desenvolvemos, também, medidas de influência, somente sob o enfoque bayesiano, utilizando a medida de Kullback Leibler. Além disso, adaptamos métodos de checagem preditiva à posteriori existentes na literatura, ao nosso modelo, utilizando medidas de discrepância apropriadas. Para a comparação de modelos, utilizamos os critérios usuais na literatura, como AIC, BIC e DIC. Realizamos diversos estudos de simulação, considerando algumas situações de interesse prático, com o intuito de comparar as estimativas bayesianas com as frequentistas, bem como avaliar o comportamento das ferramentas de diagnóstico desenvolvidas. Um conjunto de dados da área psicométrica foi analisado para ilustrar o potencial do ferramental desenvolvido
Abstract: In this work we developed the zero-one augmented rectangular beta distribution, as well as a correspondent zero-one augmented rectangular beta regression model to analyze limited-augmented data (represented by mixed random variables with limited support), which present outliers. We develop inference tools under the Bayesian and frequentist approaches. Regarding to the Bayesian inference, due the impossibility of obtaining analytically the posterior distributions of interest, we used MCMC algorithms. Concerning the frequentist estimation, we use the EM algorithm. We develop techniques of residual analysis, by using the randomized quantile residuals, under both frequentist and Bayesian approaches. We also developed influence measures, only under the Bayesian approach, by using the measure of Kullback Leibler. In addition, we adapt methods of posterior predictive checking available in the literature, to our model, using appropriate discrepancy measures. For model selection, we use the criteria commonly employed in the literature, such as AIC, BIC and DIC. We performed several simulation studies, considering some situations of practical interest, in order to compare the Bayesian and frequentist estimates, as well as to evaluate the behavior of the developed diagnostic tools. A psychometric real data set was analyzed to illustrate the performance of the developed tools
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Nourmohammadi, Mohammad. „Statistical inference with randomized nomination sampling“. Elsevier B.V, 2014. http://hdl.handle.net/1993/30150.
Der volle Inhalt der QuelleBücher zum Thema "Proportion inference"
Consortium for Mathematics and Its Applications (U.S.), Chedd-Angier Production Company, American Statistical Association und Annenberg Media, Hrsg. Against all odds--inside statistics: Disc 3, programs 9-12. S. Burlington, VT: Annenberg Media, 2011.
Den vollen Inhalt der Quelle findenInference for proportions [videorecording]. Oakville, Ont. :bMagic Lantern Communications Ltd, 1989.
Den vollen Inhalt der Quelle findenGill, Jeff, und Jonathan Homola. Issues in Polling Methodologies. Herausgegeben von Lonna Rae Atkeson und R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.11.
Der volle Inhalt der QuelleHankin, David, Michael S. Mohr und Kenneth B. Newman. Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.001.0001.
Der volle Inhalt der QuelleGolub, Jonathan. Survival Analysis. Herausgegeben von Janet M. Box-Steffensmeier, Henry E. Brady und David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0023.
Der volle Inhalt der QuelleMcCleary, Richard, David McDowall und Bradley J. Bartos. Statistical Conclusion Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0006.
Der volle Inhalt der QuelleBuchteile zum Thema "Proportion inference"
Myers, Chelsea. „Inference About a Population Proportion“. In Project-Based R Companion to Introductory Statistics, 119–30. First edition. | Boca Raton : Taylor and Francis, 2021.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429292002-ch09.
Der volle Inhalt der QuelleCowles, Mary Kathryn. „Inference for a Population Proportion“. In Springer Texts in Statistics, 49–65. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5696-4_4.
Der volle Inhalt der QuelleBest, Lisa, und Claire Goggin. „The Science of Seeing Science: Examining the Visuality Hypothesis“. In Diagrammatic Representation and Inference, 339–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86062-2_34.
Der volle Inhalt der QuelleSalinas Ruíz, Josafhat, Osval Antonio Montesinos López, Gabriela Hernández Ramírez und Jose Crossa Hiriart. „Generalized Linear Mixed Models for Proportions and Percentages“. In Generalized Linear Mixed Models with Applications in Agriculture and Biology, 209–78. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32800-8_6.
Der volle Inhalt der QuelleManalo, Emmanuel, und Laura Ohmes. „The Use of Diagrams in Planning for Report Writing“. In Diagrammatic Representation and Inference, 268–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15146-0_23.
Der volle Inhalt der QuelleHolmes, William H., und William C. Rinaman. „Inference for Proportions“. In Statistical Literacy for Clinical Practitioners, 149–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12550-3_6.
Der volle Inhalt der QuellePagano, Marcello, Kimberlee Gauvreau und Heather Mattie. „Inference on Proportions“. In Principles of Biostatistics, 323–50. 3. Aufl. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9780429340512-14.
Der volle Inhalt der QuelleHahs-Vaughn, Debbie L., und Richard G. Lomax. „Inferences About Proportions“. In Statistical Concepts, 297–343. New York, NY : Routledge, 2019.: Routledge, 2020. http://dx.doi.org/10.4324/9780429261268-8.
Der volle Inhalt der QuelleConnor, Jason T., und Peter B. Imrey. „Proportions: Inferences and Comparisons“. In Methods and Applications of Statistics in Clinical Trials, 570–94. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118596333.ch34.
Der volle Inhalt der QuelleSeber, George A. F. „Proportions, Inferences, and Comparisons“. In International Encyclopedia of Statistical Science, 1135–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_463.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Proportion inference"
Center, Julian L., Kevin H. Knuth, Ariel Caticha, Julian L. Center, Adom Giffin und Carlos C. Rodríguez. „Regression for Proportion Data“. In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. AIP, 2007. http://dx.doi.org/10.1063/1.2821266.
Der volle Inhalt der QuelleYoshida, Haruka, und Manabu Kuroki. „Proportion-based Sensitivity Analysis of Uncontrolled Confounding Bias in Causal Inference“. In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/789.
Der volle Inhalt der QuelleSimonnet, Titouan, Mame Diarra Fall, Bruno Galerne, Francis Claret und Sylvain Grangeon. „Proportion Inference Using Deep Neural Networks. Applications to X-Ray Diffraction and Hyperspectral Imaging“. In 2023 31st European Signal Processing Conference (EUSIPCO). IEEE, 2023. http://dx.doi.org/10.23919/eusipco58844.2023.10289954.
Der volle Inhalt der QuelleMa, Weijing, Fan Wang, Jingyi Zhang und Qiang Jin. „Overload Risk Evaluation of DNs with High Proportion EVs Based on Adaptive Net-based Fuzzy Inference System“. In 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2020. http://dx.doi.org/10.1109/ei250167.2020.9346905.
Der volle Inhalt der QuelleSeeaed, Mushtak, und Al-Hakam Hamdan. „BIMification of Stone Walls for Maintenance Management by Utilizing Linked Data“. In 4th International Conference on Architectural & Civil Engineering Sciences. Cihan University-Erbil, 2023. http://dx.doi.org/10.24086/icace2022/paper.879.
Der volle Inhalt der QuelleSchockaert, Steven, Yazmin Ibanez-Garcia und Victor Gutierrez-Basulto. „A Description Logic for Analogical Reasoning“. In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/281.
Der volle Inhalt der QuelleAbdul rabu, Siti nazleen, Baharuddin Aris und Zaidatun Tasir. „LEVEL OF STUDENTS' CRITICAL THINKING ENGAGEMENT IN AN ONLINE INSTRUCTIONAL MULTIMEDIA DEVELOPMENT SCENARIO-BASED DISCUSSION FORUM“. In eLSE 2017. Carol I National Defence University Publishing House, 2017. http://dx.doi.org/10.12753/2066-026x-17-089.
Der volle Inhalt der QuellePore, M., G. Gilfeather und L. Levy. „Risk Assessment in Signature Analysis“. In ISTFA 1996. ASM International, 1996. http://dx.doi.org/10.31399/asm.cp.istfa1996p0177.
Der volle Inhalt der QuelleZhang, Yuanxing, Yangbin Zhang, Kaigui Bian und Xiaoming Li. „Towards Reading Comprehension for Long Documents“. In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/638.
Der volle Inhalt der QuellePrade, Henri, und Gilles Richard. „Analogical Proportions: Why They Are Useful in AI“. In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/621.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Proportion inference"
Beland, Anne, und Robert J. Mislevy. Probability-Based Inference in a Domain of Proportional Reasoning Tasks. Fort Belvoir, VA: Defense Technical Information Center, Januar 1992. http://dx.doi.org/10.21236/ada247304.
Der volle Inhalt der QuelleRodríguez, Francisco. Cleaning Up the Kitchen Sink: On the Consequences of the Linearity Assumption for Cross-Country Growth Empirics. Inter-American Development Bank, Januar 2006. http://dx.doi.org/10.18235/0011322.
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