Relevant bibliographies by topics / Student-t

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Student-t'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Student-t"

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Mason, David M., and Qi-Man Shao. "Bootstrapping the Student t -Statistic." Annals of Probability 29, no. 4 (2001): 1435–50. http://dx.doi.org/10.1214/aop/1015345757.

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Ingrand, P. "Le test t de Student." Journal d'imagerie diagnostique et interventionnelle 1, no. 2 (2018): 81–83. http://dx.doi.org/10.1016/j.jidi.2018.02.001.

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Moreno-Arenas, Germán, Guillermo Martínez-Flórez, and Heleno Bolfarine. "Power Birnbaum-Saunders Student t distribution." Revista Integración 35, no. 1 (2017): 51–70. http://dx.doi.org/10.18273/revint.v35n1-2017004.

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Griffin, Philip. "Tightness of the Student $t$-Statistic." Electronic Communications in Probability 7 (2002): 181–90. http://dx.doi.org/10.1214/ecp.v7-1059.

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Peng, Chien-Yu, and Ya-Shan Cheng. "Student-t Processes for Degradation Analysis." Technometrics 62, no. 2 (2019): 223–35. http://dx.doi.org/10.1080/00401706.2019.1630008.

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Zhihua Zhang, Gang Wu, and E. Y. Chang. "Semiparametric Regression Using Student $t$ Processes." IEEE Transactions on Neural Networks 18, no. 6 (2007): 1572–88. http://dx.doi.org/10.1109/tnn.2007.899736.

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Dumas, Catherine, and Abebe Rorissa. "ASIS&T: The student perspective." Bulletin of the Association for Information Science and Technology 40, no. 5 (2014): 23–27. http://dx.doi.org/10.1002/bult.2014.1720400507.

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Wallace-Spurgin, Mekca. "Implementing Technology: Measuring Student Cognitive Engagement." International Journal of Technology in Education 3, no. 1 (2019): 24. http://dx.doi.org/10.46328/ijte.v3i1.13.

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In an effort provide access to devices and prepare students for the future, a small rural high school committed to becoming a high-tech school. However, data collected using the IPI-T process suggested teachers were typically the users of the technology, students were often disengaged, and teachers were asking students to participate in lower-order surface activities. Missing from the process was the implementation of the faculty collaborative sessions. The year after the initial rollout of the devices, IPI-T data was collected three times. Additionally, faculty collaborative sessions were pla
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Nugroho, Didit Budi, Agus Priyono, and Bambang Susanto. "SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL." MEDIA STATISTIKA 14, no. 1 (2021): 21–32. http://dx.doi.org/10.14710/medstat.14.1.21-32.

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The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data. In the empirical financial literature, the presence of skewness and heavy-tails have impacts on how well the GARCH-type models able to capture the financial market volatility sufficiently. This study estimates the volatility of financial asset returns based on the GARCH(1,1) model assuming Skew Normal and Skew Student-t distributions for the returns errors. The models are applied to d
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Rahayu, Puji, and Ani Widayati. "EFFECTIVENESS OF THINK PAIR SHARE AND SPONTANEOUS GROUP DISCUSSION TOWARDS PROBLEM SOLVING SKILL STUDENT OF X ACCOUNTING GRADERS SMK NEGERI 1 WONOSARI." Jurnal Pendidikan Akuntansi Indonesia 17, no. 2 (2019): 117–30. http://dx.doi.org/10.21831/jpai.v17i2.28698.

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This research is aimed to know: 1) the differences of problem solving skills in students’ learning with Think Pair Share and Spontaneous Group Discussion; 2) the effectiveness of the implementation of Think Pair Share and Spontaneous Group Discussion. This research is a quasi-experimental research involving 32 students of X AK1 and X AK3. Data collection technique was a tests. Data analysis techniques for testing the result of this research were normality test, homogeneity test, and hypothesis test with t-test. The results of this study show that: 1) There are no differences in problem solving
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Dissertations / Theses on the topic "Student-t"

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Lopes, Jocely Nascimento. "Misturas de distribuições T de student assimétricas." Universidade Federal do Amazonas, 2008. http://tede.ufam.edu.br/handle/tede/5226.

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Submitted by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-09-30T13:45:51Z No. of bitstreams: 1 Dissertação - Jocely Lopes.pdf: 2989672 bytes, checksum: 6e22a80fdd1f21213e48a66c75d7c923 (MD5)<br>Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-09-30T13:46:06Z (GMT) No. of bitstreams: 1 Dissertação - Jocely Lopes.pdf: 2989672 bytes, checksum: 6e22a80fdd1f21213e48a66c75d7c923 (MD5)<br>Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-09-30T13:46:22Z (GMT
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Englund, Jonas. "Another Student´s T-test : Proposal and evaluation of a modified T-test." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-37501.

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Rama, Vishal. "Estimating stochastic volatility models with student-t distributed errors." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32390.

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This dissertation aims to extend on the idea of Bollerslev (1987), estimating ARCH models with Student-t distributed errors, to estimating Stochastic Volatility (SV) models with Student-t distributed errors. It is unclear whether Gaussian distributed errors sufficiently account for the observed leptokurtosis in financial time series and hence the extension to examine Student-t distributed errors for these models. The quasi-maximum likelihood estimation approach introduced by Harvey (1989) and the conventional Kalman filter technique are described so that the SV model with Gaussian distributed
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Paczkowski, Remi. "Monte Carlo Examination of Static and Dynamic Student t Regression Models." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/38691.

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This dissertation examines a number of issues related to Static and Dynamic Student t Regression Models. The Static Student t Regression Model is derived and transformed to an operational form. The operational form is then examined in a series of Monte Carlo experiments. The model is judged based on its usefulness for estimation and testing and its ability to model the heteroskedastic conditional variance. It is also compared with the traditional Normal Linear Regression Model. Subsequently the analysis is broadened to a dynamic setup. The Student t Autoregressive Model is derived and a numb
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Busato, Erick Andrade. "Função de acoplamento t-Student assimetrica : modelagem de dependencia assimetrica." [s.n.], 2008. http://repositorio.unicamp.br/jspui/handle/REPOSIP/305857.

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Orientador: Luiz Koodi Hotta<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica<br>Made available in DSpace on 2018-08-12T14:00:24Z (GMT). No. of bitstreams: 1 Busato_ErickAndrade_M.pdf: 4413458 bytes, checksum: b9c4c39b4639c19e685bae736fc86c4f (MD5) Previous issue date: 2008<br>Resumo: A família de distribuições t-Student Assimétrica, construída a partir da mistura em média e variância da distribuição normal multivariada com a distribuição Inversa Gama possui propriedades desejáveis de flexibilidade para as mais divers
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Rahman, Azizur. "Bayesian prediction distributions for some linear models under student-t errors." University of Southern Queensland, Faculty of Sciences, 2007. http://eprints.usq.edu.au/archive/00003581/.

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[Abstract]: This thesis investigates the prediction distributions of future response(s), conditional on a set of realized responses for some linear models havingstudent-t error distributions by the Bayesian approach under the uniform priors. The models considered in the thesis are the multiple regression modelwith multivariate-t errors and the multivariate simple as well as multiple re-gression models with matrix-T errors. For the multiple regression model, results reveal that the prediction distribution of a single future response anda set of future responses are a univariate and multivariate
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Assumpção, Rosangela Aparecida Botinha. "Influência local em modelos geoestatísticos T-Student com aplicações a dados agrícolas." Universidade Estadual do Oeste do Parana, 2010. http://tede.unioeste.br:8080/tede/handle/tede/374.

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Made available in DSpace on 2017-05-12T14:48:22Z (GMT). No. of bitstreams: 1 Rosangela_texto.pdf: 2310887 bytes, checksum: d9e69eaef22ee697283c66446001b19e (MD5) Previous issue date: 2010-12-16<br>The presence of inconsistent observations make it improper to consider the gaussian process, as it is found in the literature. This process should be replaced by models of the symmetric distribution classes, such as the t-student distribution, which incorporates additional parameters to reduce the influence of inconsistent points. This work has developed the EM algorithm for estimating the structur
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Macerau, Walkiria Maria de Oliveira. "Comparação das distribuições α-estável, normal, t de student e Laplace assimétricas". Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/4555.

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Made available in DSpace on 2016-06-02T20:06:06Z (GMT). No. of bitstreams: 1 4185.pdf: 8236823 bytes, checksum: fc450b707396aa2c496c5373af93ef3d (MD5) Previous issue date: 2012-01-27<br>Financiadora de Estudos e Projetos<br>Abstract The asymmetric distributions has experienced great development in recent times. They are used in modeling financial data, medical, genetics and other applications. Among these distributions, the Skew normal (Azzalini, 1985) has received more attention from researchers (Genton et al., (2001), Gupta et al., (2004) and Arellano-Valle et al., (2005)). We present a
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Cintra, Flávia Maria Ravagnani Neves. "Aplicação do modelo t-student para análise dos resultados de ensaios de proficiência." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-03012018-171250/.

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Os Ensaios de Proficiência por comparação interlaboratorial têm sido um importante mecanismo para controlar a consistência dos laboratórios. Instituições do governo, como o INMETRO, têm utilizado tais mecanismos para monitorar a qualidade dos serviços prestados pelos laboratórios da Rede Brasileira de Laboratórios (RBL) e da Rede Brasileira de Calibração (RBC). Atualmente, os métodos estatísticos utilizados para analisar os resultados dos Ensaios de Proficiência estão escritos em normas técnicas, como o ISO/IEC Guide 43-1. Recentemente, Leão, Aoki e Silva (2002) propuseram um método de regress
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Souza, Aline Campos Reis de. "Modelos de regressão linear heteroscedásticos com erros t-Student : uma abordagem bayesiana objetiva." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/7540.

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Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-09-26T18:57:40Z No. of bitstreams: 1 DissACRS.pdf: 1390452 bytes, checksum: a5365fdbf745228c0174f2643b3f7267 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-27T19:59:56Z (GMT) No. of bitstreams: 1 DissACRS.pdf: 1390452 bytes, checksum: a5365fdbf745228c0174f2643b3f7267 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-27T20:00:01Z (GMT) No. of bitstreams: 1 DissACRS.pdf: 1390452 bytes, checksum: a5365fdbf745228c0174f2643b3f7267 (MD5)<br>Made available in
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Books on the topic "Student-t"

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Ahsanullah, Mohammad, B. M. Golam Kibria, and Mohammad Shakil. Normal and Student´s t Distributions and Their Applications. Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-061-4.

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Lu, Qiaoping. Medienkompetenz von Studierenden an chinesischen Hochschulen. VS, Verl. fu r Sozialwiss., 2008.

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Ceuster, Marc de. Diagnostic checking of estimation with a Student-t error density. Universiteit Antwerpen, 1992.

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Hinderliter. Student Workbook t/a Elementary Statistics for Psychology Students. McGraw-Hill Primis Custom Publishing, 1996.

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Lial. Text & Student Solution Manual T/A Algebra/Coll Students. Not Avail, 1998.

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Student Workbook t/a Elementary Statistics for Psychology Students. McGraw-Hill Primis Custom Publishing, 1996.

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Ballman, Terry L., Bill VanPatten, and James F. Lee. Student Audiocassette Program t/a Vistazos. McGraw-Hill Humanities/Social Sciences/Languages, 2001.

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Magnan, Sally Sieloff. Student Video Manual T/A Paroles. Wiley, 1998.

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McDaniel, Carl Jr. Market Research Essentials Student T/A. Wiley, 2000.

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Colander, David C. Student Problem Set t/a Economics. 6th ed. McGraw-Hill/Irwin, 2006.

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Book chapters on the topic "Student-t"

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Frost, Irasianty. "Beispiel: Student-t-Test." In essentials. Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-16258-0_4.

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Ahsanullah, Mohammad, B. M. Golam Kibria, and Mohammad Shakil. "Student’s $$t$$ t Distribution." In Normal and Student´s t Distributions and Their Applications. Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-061-4_3.

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Towndrow, Phillip Alexander, and Galyna Kogut. "Student T. Rushing, Busy, Crowded." In Studies in Singapore Education: Research, Innovation & Practice. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8727-6_11.

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Li, Xiaoyan, and Jinwen Ma. "Non-central Student-t Mixture of Student-t Processes for Robust Regression and Prediction." In Intelligent Computing Theories and Application. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84522-3_41.

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Grigelionis, Bronius. "Student-Lévy Processes." In Student’s t-Distribution and Related Stochastic Processes. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31146-8_4.

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Grigelionis, Bronius. "Student Diffusion Processes." In Student’s t-Distribution and Related Stochastic Processes. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31146-8_6.

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Schmidt, Daniel F., and Enes Makalic. "Robust Lasso Regression with Student-t Residuals." In AI 2017: Advances in Artificial Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63004-5_29.

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Frieden, B. Roy. "The Student t-Test on the Mean." In Probability, Statistical Optics, and Data Testing. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-97289-8_12.

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Frieden, B. Roy. "The Student t-Test on the Mean." In Probability, Statistical Optics, and Data Testing. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56699-8_12.

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Ahsanullah, Mohammad, B. M. Golam Kibria, and Mohammad Shakil. "Product of the Normal and Student’s $$t$$ t Densities." In Normal and Student´s t Distributions and Their Applications. Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-061-4_7.

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Conference papers on the topic "Student-t"

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Tang, Qingtao, Li Niu, Yisen Wang, et al. "Student-t Process Regression with Student-t Likelihood." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/393.

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Gaussian Process Regression (GPR) is a powerful Bayesian method. However, the performance of GPR can be significantly degraded when the training data are contaminated by outliers, including target outliers and input outliers. Although there are some variants of GPR (e.g., GPR with Student-t likelihood (GPRT)) aiming to handle outliers, most of the variants focus on handling the target outliers while little effort has been done to deal with the input outliers. In contrast, in this work, we aim to handle both the target outliers and the input outliers at the same time. Specifically, we replace t
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Chen, Yang, Feng Chen, Jing Dai, T. Charles Clancy, and Yao-Jan Wu. "Student-t Based Robust Spatio-temporal Prediction." In 2012 IEEE 12th International Conference on Data Mining (ICDM). IEEE, 2012. http://dx.doi.org/10.1109/icdm.2012.135.

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Gayen, Atin, and M. Ashok Kumar. "Generalized Estimating Equation for the Student-t Distributions." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437622.

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Berntorp, Karl, and Stefano Di Cairano. "Approximate Noise-Adaptive Filtering Using Student-t Distributions." In 2018 Annual American Control Conference (ACC). IEEE, 2018. http://dx.doi.org/10.23919/acc.2018.8430902.

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Takahashi, Hiroshi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada, and Satoshi Yagi. "Student-t Variational Autoencoder for Robust Density Estimation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/374.

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We propose a robust multivariate density estimator based on the variational autoencoder (VAE). The VAE is a powerful deep generative model, and used for multivariate density estimation. With the original VAE, the distribution of observed continuous variables is assumed to be a Gaussian, where its mean and variance are modeled by deep neural networks taking latent variables as their inputs. This distribution is called the decoder. However, the training of VAE often becomes unstable. One reason is that the decoder of VAE is sensitive to the error between the data point and its estimated mean whe
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FORBES, ALISTAIR B. "ASYMPTOTIC LEAST SQUARES AND STUDENT-T SAMPLING DISTRIBUTIONS." In Advanced Mathematical and Computational Tools in Metrology. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812774187_0036.

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Tzikas, Dimitris, Aristidis Likas, and Nikolaos Galatsanos. "Variational Bayesian Blind Image Deconvolution with Student-T Priors." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4378903.

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Imran, Muahmmad, Zahid Manzoor, Sajid Ali, and Qamar Abbas. "Modified Particle Swarm Optimization with student T mutation (STPSO)." In 2011 International Conference on Computer Networks and Information Technology (ICCNIT). IEEE, 2011. http://dx.doi.org/10.1109/iccnit.2011.6020944.

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Mairgiotis, Antonis, Lisimachos P. Kondi, and Yongyi Yang. "Dct/dwt blind multiplicative watermarking through student-t distribution." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296335.

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Tang, Qingtao, Tao Dai, Li Niu, Yisen Wang, Shu-Tao Xia, and Jianfei Cai. "Robust Survey Aggregation with Student-t Distribution and Sparse Representation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/394.

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Most existing survey aggregation methods assume that the sample data follow Gaussian distribution. However, these methods are sensitive to outliers, due to the thin-tailed property of the Gaussian distribution. To address this issue, we propose a robust survey aggregation method based on Student-t distribution and sparse representation. Specifically, we assume that the samples follow Student-$t$ distribution, instead of the common Gaussian distribution. Due to the Student-t distribution, our method is robust to outliers, which can be explained from both Bayesian point of view and non-Bayesian
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Reports on the topic "Student-t"

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Gibbons, Robert D., Donald Hedeker, and R. D. Bock. Multivariate Generalizations of Student's t-Distribution. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada229128.

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Roschelle, Jeremy, Britte Haugan Cheng, Nicola Hodkowski, Julie Neisler, and Lina Haldar. Evaluation of an Online Tutoring Program in Elementary Mathematics. Digital Promise, 2020. http://dx.doi.org/10.51388/20.500.12265/94.

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Many students struggle with mathematics in late elementary school, particularly on the topic of fractions. In a best evidence syntheses of research on increasing achievement in elementary school mathematics, Pelligrini et al. (2018) highlighted tutoring as a way to help students. Online tutoring is attractive because costs may be lower and logistics easier than with face-to-face tutoring. Cignition developed an approach that combines online 1:1 tutoring with a fractions game, called FogStone Isle. The game provides students with additional learning opportunities and provides tutors with inform
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T he formation of the student’s psychological health in the context of competence - based approach. O.V. Lebedeva, 2015. http://dx.doi.org/10.14526/01_1111_13.

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