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

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Student-t.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Student-t"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Moreno-Arenas, Germán, Guillermo Martínez-Flórez, and Heleno Bolfarine. "Power Birnbaum-Saunders Student t distribution." Revista Integración 35, no. 1 (August 10, 2017): 51–70. http://dx.doi.org/10.18273/revint.v35n1-2017004.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Peng, Chien-Yu, and Ya-Shan Cheng. "Student-t Processes for Degradation Analysis." Technometrics 62, no. 2 (July 22, 2019): 223–35. http://dx.doi.org/10.1080/00401706.2019.1630008.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
Abstract:
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 planned and facilitated within one week of collection data. Participating in each faculty collaborative session, teachers (a) became familiar with the IPI-T Rubric and Protocols, (b) analyzed and discussed the data, (c) identified high-quality examples of student learning that foster student engagement with technology, (d) designed high-quality lessons that foster student engagement with technology, (e) compared longitudinal data and set goals for future data collection using the IPI-T tool. An analysis of the data revealed when implementing the IPI-T process with fidelity teacher and student technology use increased as did student cognitive engagement when using technology. In addition, it was found that students use technology for information searches the majority of the time rather than media development or to collaborate among peers for example, which is associated with higher-levels of cognitive engagement.
APA, Harvard, Vancouver, ISO, and other styles
9

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 (April 16, 2021): 21–32. http://dx.doi.org/10.14710/medstat.14.1.21-32.

Full text
Abstract:
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 daily returns of FTSE100 and IBEX35 stock indices from January 2000 to December 2017. The model parameters are estimated by using the Generalized Reduced Gradient Non-Linear method in Excel’s Solver and also the Adaptive Random Walk Metropolis method implemented in Matlab. The estimation results from fitting the models to real data demonstrate that Excel’s Solver is a promising way for estimating the parameters of the GARCH(1,1) models with non-Normal distribution, indicated by the accuracy of the estimation of Excel’s Solver. The fitting performance of models is evaluated by using log-likelihood ratio test and it indicates that the GARCH(1,1) model with Skew Student-t distribution provides the best fitting, followed by Student-t, Skew-Normal, and Normal distributions.
APA, Harvard, Vancouver, ISO, and other styles
10

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 (December 9, 2019): 117–30. http://dx.doi.org/10.21831/jpai.v17i2.28698.

Full text
Abstract:
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 skill between students with Think Pair Share and student with Spontaneous Group Discussion student of X Accounting graders SMK Negeri 1 Wonosari. It proved by post-test on hypothesis test with the signification in the result of independent sample t-test is greater than α = 0.05 (0,475 0,05) and t count t table (0,719 2,000). 2)There are no differences in effectiveness between students with Think Pair and student with Spontaneous Group Discussion student of X Accounting graders SMK Negeri 1 Wonosari. It proved by independent samples t-test for gain score is greater than α = 0.05 (0.786 0.05) and t count t table (0,272 2,000). Keywords: Introduction to Accounting learning, TPS, SGD, Problem Solving Skill
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Student-t"

1

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

Full text
Abstract:
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)
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)
Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-09-30T13:46:22Z (GMT) No. of bitstreams: 1 Dissertação - Jocely Lopes.pdf: 2989672 bytes, checksum: 6e22a80fdd1f21213e48a66c75d7c923 (MD5)
Made available in DSpace on 2016-09-30T13:46:22Z (GMT). No. of bitstreams: 1 Dissertação - Jocely Lopes.pdf: 2989672 bytes, checksum: 6e22a80fdd1f21213e48a66c75d7c923 (MD5) Previous issue date: 2008-12-22
Não informada
In this work we consider the estimation of parameters of a nite mixture of skew Student-t distributions, via EM algorithm. The main goals of this dissertation is to show a detailed description of the EM algorithm applied to this model and to evaluate the consistency of the estimator. A data set concerning the Gross Domestic Product per capita (Human Development Report), previously studied in the related literature, is analyzed.
Este trabalho trata do problema de estimar parâmetros de uma mistura nita de densidades t-assimétricas. Como ferramenta para a estimação foi usado o algoritmo EM. Foi avaliada a consistência desses estimadores e realizado um experimento de aplicação da teoria desenvolvida para uma modelagem com dados reais utilizando um conjunto analisado anteriormente na literatura, relativo ao PIB per capita. Os objetivos centrais desse trabalho são apresentar uma descrição detalhada do método de estimação, via algoritmo EM, dos parâmetros do modelo nito de mistura de densidades t-assimétricas e avaliar através de um estudo de simulação se o estimador obtido é consistente.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Full text
Abstract:
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 errors and SV model with Student-t distributed errors can be estimated. Estimation of GARCH (1,1) models is also described using the method maximum likelihood. The empirical study estimated four models using data on four different share return series and one index return, namely: Anglo American, BHP, FirstRand, Standard Bank Group and JSE Top 40 index. The GARCH and SV model with Student-t distributed errors both perform best on the series examined in this dissertation. The metric used to determine the best performing model was the Akaike information criterion (AIC).
APA, Harvard, Vancouver, ISO, and other styles
4

Paczkowski, Remi. "Monte Carlo Examination of Static and Dynamic Student t Regression Models." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/38691.

Full text
Abstract:
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 number of its operational forms are considered. Three forms are selected for a detailed examination in a series of Monte Carlo experiments. The models’ usefulness for estimation and testing is evaluated, as well as their ability to model the conditional variance. The models are also compared with the traditional Dynamic Linear Regression Model.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
5

Busato, Erick Andrade. "Função de acoplamento t-Student assimetrica : modelagem de dependencia assimetrica." [s.n.], 2008. http://repositorio.unicamp.br/jspui/handle/REPOSIP/305857.

Full text
Abstract:
Orientador: Luiz Koodi Hotta
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
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
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 diversas formas de assimetria. Essas propriedades são exploradas na construção de funções de acoplamento que possuem dependência assimétrica. Neste trabalho são estudadas as características e propriedades da distribuição t-Student Assimétrica e a construção da respectiva função de acoplamento, fazendo-se uma apresentação de diferentes estruturas de dependência que pode originar, incluindo assimetrias da dependência nas caudas. São apresentados métodos de estimação de parâmetros das funções de acoplamento, com aplicações até a terceira dimensão da cópula. Essa função de acoplamento é utilizada para compor um modelo ARMA-GARCHCópula com marginais de distribuição t-Student Assimétrica, que será ajustado para os logretornos de preços do Petróleo e da Gasolina, e log-retornos do Índice de Óleo AMEX, buscando o melhor ajuste, principalmente, para a dependência nas caudas das distribuições de preços. Esse modelo será comparado, através de medidas de Valor em Risco e AIC, além de outras medidas de bondade de ajuste, com o modelo de Função de Acoplamento t-Student Simétrico.
Abstract: The Skewed t-Student distribution family, constructed upon the multivariate normal mixture distribution, known as mean-variance mixture, composed with the Inverse-Gamma distribution, has many desirable flexibility properties for many distribution asymmetry structures. These properties are explored by constructing copula functions with asymmetric dependence. In this work the properties and characteristics of the Skewed t-Student distribution and the construction of a respective copula function are studied, presenting different dependence structures that the copula function generates, including tail dependence asymmetry. Parameter estimation methods are presented for the copula, with applications up to the 3rd dimension. This copula function is used to compose an ARMAGARCH- Copula model with Skewed t-Student marginal distribution that is adjusted to logreturns of Petroleum and Gasoline prices and log-returns of the AMEX Oil Index, emphasizing the return's tail distribution. The model will be compared, by the means of the VaR (Value at Risk) and Akaike's Information Criterion, along with other Goodness-of-fit measures, with models based on the Symmetric t-Student Copula.
Mestrado
Mestre em Estatística
APA, Harvard, Vancouver, ISO, and other styles
6

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/.

Full text
Abstract:
[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 Student-t distributions respectively with appropriate location, scale and shape parameters.The shape parameter of these prediction distributions depend on the size of the realized responses vector and the dimension of the regression parameters' vector, but do not depend on the degrees of freedom of the error distribu-tion. In the multivariate case, the distribution of a future responses matrix from the future model, conditional on observed responses matrix from the realized model for both the multivariate simple and multiple regression mod-els is matrix-T distribution with appropriate location matrix, scale factors and shape parameter. The results for both of these models indicate that prediction distributions depend on the realized responses only through the sample regression matrix and the sample residual sum of squares and products matrix. The prediction distribution also depends on the design matricesof the realized as well as future models. The shape parameter of the prediction distribution of the future responses matrix depends on size of the realized sample and the number of regression parameters of the multivariatemodel. Furthermore, the prediction distributions are derived by the Bayesian method as multivariate-t and matrix-T are identical to those obtained under normal errors' distribution by the di®erent statistical methods such as the classical, structural distribution and structural relations of the model approaches. This indicates not only the inference robustness with respect todepartures from normal error to Student-t error distributions, but also indicates that the Bayesian approach with a uniform prior is competitive withother statistical methods in the derivation of prediction distribution.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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
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 structure of the spatial dependence of the parameters and of the spatial linear model, assuming that the process shows t-student n-varied distribution. This distribution has the degree of freedom v as the additional parameter, which has been considered to be fixed in this research. Techniques to diagnose influence are used after the estimation of parameters, in order to assess the quality of the adjustment of the model by the assumptions made and for the robustness of the results of the estimates when there are disturbances in the model or data. In the present work, diagnostic techniques for the assessment of local influence in linear spatial models have been developed, considering the process with t-student n-varied distribution. The usual diagnostic technique evaluates the withdrawing of the likelihood rate by the function of the likelihood logarithm. In this proposal, in addition to considering the usual technique, we use the withdrawing of the likelihood by Q-displacement of the complete likelihood. The application of the usual technique and of the one proposed here are illustrated through the analyses of both simulated and real data, provenient of agricultural experiments.
A presença de observações discrepantes torna imprópria a análise do processo gaussiano, sendo assim, como é encontrado na literatura, esse processo deve ser substituído por modelos da classe das distribuições simétricas, tal como a distribuição t-student, que incorpora parâmetros adicionais para reduzir a influência dos pontos discrepantes. Neste trabalho, assumiu-se que o processo apresenta distribuição t-student n-variada. Essa distribuição tem como parâmetro adicional o grau de liberdade v, que aqui considerou-se fixo. Dessa forma, desenvolveu-se o algoritmo EM e o algoritmo de NR para a estimação dos parâmetros da estrutura de dependência espacial e do modelo espacial linear. Após a estimação dos parâmetros, utilizou-se duas técnicas de diagnósticos de influência local, ambas com o intuito de avaliar a qualidade do ajuste do modelo pelas suposições feitas e pela robustez dos resultados das estimativas quando há perturbações no modelo ou nos dados. A primeira técnica, denominada "usual", já utilizada por diversos autores, avalia o afastamento da verossimilhança pela função do logaritmo da verossimilhança e a segunda técnica que aqui apresentamos propõe a análise de influência local pelo Q-afastamento da função de verossimilhança para dados completos. Essas técnicas permitiram verificar a influência no afastamento da verossimilhança, na matriz de covariância, no preditor linear e nos valores preditos por meio da análise gráfica. Para ilustrar a aplicação da técnica usual e da nossa proposta, realizou-se a análise de dados simulados e dados reais provenientes de experimentos agrícolas.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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
Financiadora de Estudos e Projetos
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 comparative study of _-stable distributions, Skew normal, Skew t de Student and Skew Laplace. The _-stable distribution is studied by Nolan (2009) and proposed by Gonzalez et al., (2009) in the context of genetic data. For some real datasets, in areas such as financial, genetics and commodities, we test which distribution best fits the data. We compare these distributions using the model selection criteria AIC and BIC.
As distribuições assimétricas tem experimentado grande desenvolvimento nos tempos recentes. Elas são utilizadas na modelagem de dados financeiros, médicos e genéticos entre outras aplicações. Dentre essas distribuições, a normal assimétrica (Azzalini, 1985) tem recebido mais atenção dos pesquisadores (Genton et al., (2001), Gupta et al., (2004) e Arellano-Valle et al., (2005)). Nesta dissertação, apresentamos um estudo comparativo das distribuições _-estável, normal , t de Student e Laplace assimétricas. A distribuição _-estável estudada por Nolan (2009) é proposta por Gonzalez et al., (2009) no contexto de dados genéticos. Neste trabalho, também apresentamos como verificar a assimetria de uma distribuição, descrevemos algumas características das distribuições assimétricas em estudo, e comparamos essas distribuições utilizando os critérios de seleção de modelos AIC e BIC..
APA, Harvard, Vancouver, ISO, and other styles
9

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/.

Full text
Abstract:
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 regressão para testar a competência dos laboratórios, utilizando a distribuição normal multivariada para modelar os dados e estabelecer testes estatísticos. Como em medições a presença de valores extremos é constante, vamos modelar os dados utilizando a distribuição t-student, para acomodar tais valores extremos. Neste modelo, estamos interessados em estimar o grau de liberdade e o parâmetro de tendência da medição do laboratório com respeito ao valor de referência, já que os laboratórios vão utilizar sistemas de medições similares para medir o mesmo item, com incertezas determinadas por um processo de calibração. Encontraremos os estimadores de máxima verossimilhança e de momentos para estes dois parâmetros e vamos desenvolver um teste para avaliarmos a consistência das medições dos laboratórios. No final, a título de aplicação, vamos analisar os dados obtidos pela REMESP (Rede de Metrologia de São Paulo) na área de eletricidade, onde vamos medir a tensão DC de um multímetro digital.
The proficiency essays by interlaboratorial comparison have been an important mechanism to control the consistency of the measurements of the laboratories. Government institutions, such as INMETRO, have used such mechanisms to monitor the quality of the laboratories services supplied by the Laboratories Brazilian Network (RBL) and by the Calibration Brazilian Network (RBC). Currently. the statistical methods used to analyze the results of the Proficiency Essays are described in technical norms, like the ISO/IEC Guide 43-1. Recently, Leão, Aoki and Silva (2002) proposed a regression method to test the laboratories ability, using the multivariate normal distribution to fit the data and to establish statistical tests. Like in measurements the presence of extreme values is constant, we are modelling the data using the multivariate t-student distribution, to accommodate such extreme values. In this model, we are interested in estimating the degrees of freedom and the tendency parameter of the laboratory measurement relating to the reference value, since the laboratories are using similar measurements systems to measure the same item, with standard deviation determined by a calibration process. We are finding the maximum likelihood and moments estimators for these two parameters and are developing a test to evaluate the laboratories measurements consistency. At the end, we are analyzing the obtained data for the REMESP (São Paulo Metrology Network) in the electrical area, where we are measuring the digital multimeter DC tension.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-09-26T18:57:40Z No. of bitstreams: 1 DissACRS.pdf: 1390452 bytes, checksum: a5365fdbf745228c0174f2643b3f7267 (MD5)
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)
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)
Made available in DSpace on 2016-09-27T20:00:08Z (GMT). No. of bitstreams: 1 DissACRS.pdf: 1390452 bytes, checksum: a5365fdbf745228c0174f2643b3f7267 (MD5) Previous issue date: 2016-02-18
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
In this work , we present an extension of the objective bayesian analysis made in Fonseca et al. (2008), based on Je reys priors for linear regression models with Student t errors, for which we consider the heteroscedasticity assumption. We show that the posterior distribution generated by the proposed Je reys prior, is proper. Through simulation study , we analyzed the frequentist properties of the bayesian estimators obtained. Then we tested the robustness of the model through disturbances in the response variable by comparing its performance with those obtained under another prior distributions proposed in the literature. Finally, a real data set is used to analyze the performance of the proposed model . We detected possible in uential points through the Kullback -Leibler divergence measure, and used the selection model criterias EAIC, EBIC, DIC and LPML in order to compare the models.
Neste trabalho, apresentamos uma extensão da análise bayesiana objetiva feita em Fonseca et al. (2008), baseada nas distribuicões a priori de Je reys para o modelo de regressão linear com erros t-Student, para os quais consideramos a suposicão de heteoscedasticidade. Mostramos que a distribuiçãoo a posteriori dos parâmetros do modelo regressão gerada pela distribuição a priori e própria. Através de um estudo de simulação, avaliamos as propriedades frequentistas dos estimadores bayesianos e comparamos os resultados com outras distribuições a priori encontradas na literatura. Além disso, uma análise de diagnóstico baseada na medida de divergência Kullback-Leiber e desenvolvida com analidade de estudar a robustez das estimativas na presença de observações atípicas. Finalmente, um conjunto de dados reais e utilizado para o ajuste do modelo proposto.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Student-t"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Student-t"

1

Frost, Irasianty. "Beispiel: Student-t-Test." In essentials, 13–15. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-16258-0_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Ahsanullah, Mohammad, B. M. Golam Kibria, and Mohammad Shakil. "Student’s $$t$$ t Distribution." In Normal and Student´s t Distributions and Their Applications, 51–62. Paris: Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-061-4_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Towndrow, Phillip Alexander, and Galyna Kogut. "Student T. Rushing, Busy, Crowded." In Studies in Singapore Education: Research, Innovation & Practice, 111–17. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8727-6_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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, 499–511. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84522-3_41.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Grigelionis, Bronius. "Student-Lévy Processes." In Student’s t-Distribution and Related Stochastic Processes, 41–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31146-8_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Grigelionis, Bronius. "Student Diffusion Processes." In Student’s t-Distribution and Related Stochastic Processes, 57–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31146-8_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Schmidt, Daniel F., and Enes Makalic. "Robust Lasso Regression with Student-t Residuals." In AI 2017: Advances in Artificial Intelligence, 365–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63004-5_29.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Frieden, B. Roy. "The Student t-Test on the Mean." In Probability, Statistical Optics, and Data Testing, 307–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-97289-8_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Frieden, B. Roy. "The Student t-Test on the Mean." In Probability, Statistical Optics, and Data Testing, 321–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56699-8_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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, 103–11. Paris: Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-061-4_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Student-t"

1

Tang, Qingtao, Li Niu, Yisen Wang, Tao Dai, Wangpeng An, Jianfei Cai, and Shu-Tao Xia. "Student-t Process Regression with Student-t Likelihood." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/393.

Full text
Abstract:
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 the Gaussian noise in GPR with independent Student-t noise to cope with the target outliers. Moreover, to enhance the robustness w.r.t. the input outliers, we use a Student-t Process prior instead of the common Gaussian Process prior, leading to Student-t Process Regression with Student-t Likelihood (TPRT). We theoretically show that TPRT is more robust to both input and target outliers than GPR and GPRT, and prove that both GPR and GPRT are special cases of TPRT. Various experiments demonstrate that TPRT outperforms GPR and its variants on both synthetic and real datasets.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/374.

Full text
Abstract:
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 when its estimated variance is almost zero. We solve this instability problem by making the decoder robust to the error using a Bayesian approach to the variance estimation: we set a prior for the variance of the Gaussian decoder, and marginalize it out analytically, which leads to proposing the Student-t VAE. Numerical experiments with various datasets show that training of the Student-t VAE is robust, and the Student-t VAE achieves high density estimation performance.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/394.

Full text
Abstract:
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 point of view. In addition, inspired by James-Stain estimator (JS) and Compressive Averaging (CAvg), we propose to sparsely represent the global mean vector by an adaptive basis comprising both data-specific basis and combined generic bases. Theoretically, we prove that JS and CAvg are special cases of our method. Extensive experiments demonstrate that our proposed method achieves significant improvement over the state-of-the-art methods on both synthetic and real datasets.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Student-t"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
Abstract:
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 information that they can use to plan tutoring sessions. A randomized controlled trial investigated the research question: Do students who participate in online tutoring and a related mathematical game learn more about fractions than students who only have access to the game? Participants were 144 students from four schools, all serving low-income students with low prior mathematics achievement. In the Treatment condition, students received 20-25 minute tutoring sessions twice per week for an average of 18 sessions and also played the FogStone Isle game. In the Control condition, students had access to the game, but did not play it often. Control students did not receive tutoring. Students were randomly assigned to condition after being matched on pre-test scores. The same diagnostic assessment was used as a pre-test and as a post-test. The planned analysis looked for differences in gain scores ( post-test minus pre-test scores) between conditions. We conducted a t-test on the aggregate gain scores, comparing conditions; the results were statistically significant (t = 4.0545, df = 132.66, p-value < .001). To determine an effect size, we treated each site as a study in a meta-analysis. Using gain scores, the effect size was g=+.66. A more sophisticated treatment of the pooled standard deviation resulted in a corrected effect size of g=.46 with a 95% confidence interval of [+.23,+.70]. Students who received online tutoring and played the related Fog Stone Isle game learned more; our research found the approach to be efficacious. The Pelligrini et al. (2018) meta-analysis of elementary math tutoring programs found g = .26 and was based largely on face-to-face tutoring studies. Thus, this study compares favorably to prior research on face-to-face mathematics tutoring with elementary students. Limitations are discussed; in particular, this is an initial study of an intervention under development. Effects could increase or decrease as development continues and the program scales. Although this study was planned long before the current pandemic, results are particularly timely now that many students are at home under shelter-in-place orders due to COVID-19. The approach taken here is feasible for students at home, with tutors supporting them from a distance. It is also feasible in many other situations where equity could be addressed directly by supporting students via online tutors.
APA, Harvard, Vancouver, ISO, and other styles
3

T he formation of the student’s psychological health in the context of competence - based approach. O.V. Lebedeva, June 2015. http://dx.doi.org/10.14526/01_1111_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Student-t' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography