Relevant bibliographies by topics / Jeffreys prior

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Academic literature on the topic 'Jeffreys prior'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Jeffreys prior"

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Poirier, Dale. "Jeffreys' prior for logit models." Journal of Econometrics 63, no. 2 (August 1994): 327–39. http://dx.doi.org/10.1016/0304-4076(93)01556-2.

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Millar, Russell B. "Reference priors for Bayesian fisheries models." Canadian Journal of Fisheries and Aquatic Sciences 59, no. 9 (September 1, 2002): 1492–502. http://dx.doi.org/10.1139/f02-108.

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Bayesian models require the specification of prior distributions for all unknown parameters, and this formal utilization of prior knowledge (if any) can be used to great advantage in some fisheries. However, regardless of whether prior knowledge about model parameters is available, specification of prior distributions is seldom unequivocal. This work addresses the problem of specifying default priors for several common fisheries models. To maintain consistency of terminology with the statistical literature, such priors are herein called reference priors to recognize that they can be interpreted as providing a sensible reference point against which the implications of alternative priors can be compared. Here, the Jeffreys' prior is demonstrated for the Ricker and Beverton–Holt stock–recruit curves, von Bertalanffy growth curve, Schaefer surplus production model, and sequential population analysis. The Jeffreys' priors for relevant derived parameters are demonstrated, including the steepness parameter of the Beverton–Holt stock–recruit curve. The sequential population analysis example is used to show that the Jeffreys' prior should not be automatically accepted as a reference prior in all models—this needs to be decided on a case-by-case basis.
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Zivot, Eric. "A Bayesian Analysis Of The Unit Root Hypothesis Within An Unobserved Components Model." Econometric Theory 10, no. 3-4 (August 1994): 552–78. http://dx.doi.org/10.1017/s0266466600008665.

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In this paper we extend some of Phillips's [4] results to nonlinear unobserved components models and develop a posterior odds ratio test of the unit root hypothesis based on flat and Jeffreys priors. In contrast to the analysis presented by Schotman and van Dijk [9], we utilize a nondegenerate structural representation of the components model that allows us to determine well-behaved Jeffreys priors, posterior densities under flat priors and Jeffreys priors, and posterior odds ratios for the unit root hypothesis without a proper prior for the level parameter. The analysis highlights the importance of the treatment of initial values for inference concerning stationarity and unit roots.
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Journal, Baghdad Science. "Comparison of Maximum Likelihood and some Bayes Estimators for Maxwell Distribution based on Non-informative Priors." Baghdad Science Journal 10, no. 2 (June 2, 2013): 480–88. http://dx.doi.org/10.21123/bsj.10.2.480-488.

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In this paper, Bayes estimators of the parameter of Maxwell distribution have been derived along with maximum likelihood estimator. The non-informative priors; Jeffreys and the extension of Jeffreys prior information has been considered under two different loss functions, the squared error loss function and the modified squared error loss function for comparison purpose. A simulation study has been developed in order to gain an insight into the performance on small, moderate and large samples. The performance of these estimators has been explored numerically under different conditions. The efficiency for the estimators was compared according to the mean square error MSE. The results of comparison by MSE show that the efficiency of Bayes estimators of the shape parameter of the Maxwell distribution decreases with the increase of Jeffreys prior constants. The results also show that values of Bayes estimators are almost close to the maximum likelihood estimator when the Jeffreys prior constants are small, yet they are identical in some certain cases. Comparison with respect to loss functions show that Bayes estimators under the modified squared error loss function has greater MSE than the squared error loss function especially with the increase of r.
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Rainey, Carlisle. "Dealing with Separation in Logistic Regression Models." Political Analysis 24, no. 3 (2016): 339–55. http://dx.doi.org/10.1093/pan/mpw014.

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When facing small numbers of observations or rare events, political scientists often encounter separation, in which explanatory variables perfectly predict binary events or nonevents. In this situation, maximum likelihood provides implausible estimates and the researcher might want incorporate some form of prior information into the model. The most sophisticated research uses Jeffreys’ invariant prior to stabilize the estimates. While Jeffreys’ prior has the advantage of being automatic, I show that it often provides too much prior information, producing smaller point estimates and narrower confidence intervals than even highly skeptical priors. To help researchers assess the amount of information injected by the prior distribution, I introduce the concept of a partial prior distribution and develop the tools required to compute the partial prior distribution of quantities of interest, estimate the subsequent model, and summarize the results.
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Khooriphan, Wansiri, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence Intervals for the Ratio of Variances of Delta-Gamma Distributions with Applications." Axioms 11, no. 12 (November 30, 2022): 689. http://dx.doi.org/10.3390/axioms11120689.

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Since rainfall data often contain zero observations, the ratio of the variances of delta-gamma distributions can be used to compare the rainfall dispersion between two rainfall datasets. To this end, we constructed the confidence interval for the ratio of the variances of two delta-gamma distributions by using the fiducial quantity method, Bayesian credible intervals based on the Jeffreys, uniform, or normal-gamma-beta priors, and highest posterior density (HPD) intervals based on the Jeffreys, uniform, or normal-gamma-beta priors. The performances of the proposed confidence interval methods were evaluated in terms of their coverage probabilities and average lengths via Monte Carlo simulation. Our findings show that the HPD intervals based on Jeffreys prior and the normal-gamma-beta prior are both suitable for datasets with a small and large probability of containing zeros, respectively. Rainfall data from Phrae province, Thailand, are used to illustrate the practicability of the proposed methods with real data.
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Jiang, Ruichao, Javad Tavakoli, and Yiqiang Zhao. "Weyl Prior and Bayesian Statistics." Entropy 22, no. 4 (April 20, 2020): 467. http://dx.doi.org/10.3390/e22040467.

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When using Bayesian inference, one needs to choose a prior distribution for parameters. The well-known Jeffreys prior is based on the Riemann metric tensor on a statistical manifold. Takeuchi and Amari defined the α -parallel prior, which generalized the Jeffreys prior by exploiting a higher-order geometric object, known as a Chentsov–Amari tensor. In this paper, we propose a new prior based on the Weyl structure on a statistical manifold. It turns out that our prior is a special case of the α -parallel prior with the parameter α equaling − n , where n is the dimension of the underlying statistical manifold and the minus sign is a result of conventions used in the definition of α -connections. This makes the choice for the parameter α more canonical. We calculated the Weyl prior for univariate Gaussian and multivariate Gaussian distribution. The Weyl prior of the univariate Gaussian turns out to be the uniform prior.
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D’Andrea, Amanda M. E., Vera L. D. Tomazella, Hassan M. Aljohani, Pedro L. Ramos, Marco P. Almeida, Francisco Louzada, Bruna A. W. Verssani, Amanda B. Gazon, and Ahmed Z. Afify. "Objective bayesian analysis for multiple repairable systems." PLOS ONE 16, no. 11 (November 23, 2021): e0258581. http://dx.doi.org/10.1371/journal.pone.0258581.

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This article focus on the analysis of the reliability of multiple identical systems that can have multiple failures over time. A repairable system is defined as a system that can be restored to operating state in the event of a failure. This work under minimal repair, it is assumed that the failure has a power law intensity and the Bayesian approach is used to estimate the unknown parameters. The Bayesian estimators are obtained using two objective priors know as Jeffreys and reference priors. We proved that obtained reference prior is also a matching prior for both parameters, i.e., the credibility intervals have accurate frequentist coverage, while the Jeffreys prior returns unbiased estimates for the parameters. To illustrate the applicability of our Bayesian estimators, a new data set related to the failures of Brazilian sugar cane harvesters is considered.
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Uhlig, Harald. "On Jeffreys Prior when Using the Exact Likelihood Function." Econometric Theory 10, no. 3-4 (August 1994): 633–44. http://dx.doi.org/10.1017/s0266466600008707.

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In this paper, we calculate Jeffreys prior for an AR(1) process with and without a constant and a time trend when using the exact likelihood function. We show how this prior can be calculated for the explosive region, even though the unconditional variance of the process is infinite. The calculations lend additional support to the Schotman-van Dijk [6] procedure for restricting the location and the variance of the time trend coefficient. The results show that flat priors are reasonable for the nonexplosive region in an AR(1) without a constant and a time trend where the variance is known and the initial observation is zero, i.e., for the special case studied by Sims and Uhlig [7]. Differences to a flat prior analysis remain in particular for nonzero initial observations, however. For the explosive region, the unconditional prior diverges as the root diverges, supporting findings by Phillips [4]. This paper thus provides a useful perspective as well as some reconciliation for the different stands taken in the literature about priors and Bayesian inference for potentially nonstationary time series.
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Kwek, L. C., C. H. Oh, and Xiang-Bin Wang. "Quantum Jeffreys prior for displaced squeezed thermal states." Journal of Physics A: Mathematical and General 32, no. 37 (September 6, 1999): 6613–18. http://dx.doi.org/10.1088/0305-4470/32/37/310.

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Dissertations / Theses on the topic "Jeffreys prior"

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Hornik, Kurt, and Bettina Grün. "On conjugate families and Jeffreys priors for von Mises-Fisher distributions." Elsevier, 2013. http://dx.doi.org/10.1016/j.jspi.2012.11.003.

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This paper discusses characteristics of standard conjugate priors and their induced posteriors in Bayesian inference for von Mises-Fisher distributions, using either the canonical natural exponential family or the more commonly employed polar coordinate parameterizations. We analyze when standard conjugate priors as well as posteriors are proper, and investigate the Jeffreys prior for the von Mises-Fisher family. Finally, we characterize the proper distributions in the standard conjugate family of the (matrixvalued) von Mises-Fisher distributions on Stiefel manifolds.
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Bioche, Christèle. "Approximation de lois impropres et applications." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22626/document.

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Le but de cette thèse est d’étudier l’approximation d’a priori impropres par des suites d’a priori propres. Nous définissons un mode de convergence sur les mesures de Radon strictement positives pour lequel une suite de mesures de probabilité peut admettre une mesure impropre pour limite. Ce mode de convergence, que nous appelons convergence q-vague, est indépendant du modèle statistique. Il permet de comprendre l’origine du paradoxe de Jeffreys-Lindley. Ensuite, nous nous intéressons à l’estimation de la taille d’une population. Nous considérons le modèle du removal sampling. Nous établissons des conditions nécessaires et suffisantes sur un certain type d’a priori pour obtenir des estimateurs a posteriori bien définis. Enfin, nous montrons à l’aide de la convergence q-vague, que l’utilisation d’a priori vagues n’est pas adaptée car les estimateurs obtenus montrent une grande dépendance aux hyperparamètres
The purpose of this thesis is to study the approximation of improper priors by proper priors. We define a convergence mode on the positive Radon measures for which a sequence of probability measures could converge to an improper limiting measure. This convergence mode, called q-vague convergence, is independant from the statistical model. It explains the origin of the Jeffreys-Lindley paradox. Then, we focus on the estimation of the size of a population. We consider the removal sampling model. We give necessary and sufficient conditions on the hyperparameters in order to have proper posterior distributions and well define estimate of abundance. In the light of the q-vague convergence, we show that the use of vague priors is not appropriate in removal sampling since the estimates obtained depend crucially on hyperparameters
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Nogarotto, Danilo Covaes 1987. "Inferência bayesiana em modelos de regressão beta e beta inflacionados." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306790.

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Orientador: Caio Lucidius Naberezny Azevedo
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
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Resumo: No presente trabalho desenvolvemos ferramentas de inferência bayesiana para modelos de regressão beta e beta inflacionados, em relação à estimação paramétrica e diagnóstico. Trabalhamos com modelos de regressão beta não inflacionados, inflacionados em zero ou um e inflacionados em zero e um. Devido à impossibilidade de obtenção analítica das posteriores de interesse, tais ferramentas foram desenvolvidas através de algoritmos MCMC. Para os parâmetros da estrutura de regressão e para o parâmetro de precisão exploramos a utilização de prioris comumente empregadas em modelos de regressão, bem como prioris de Jeffreys e de Jeffreys sob independência. Para os parâmetros das componentes discretas, consideramos prioris conjugadas. Realizamos diversos estudos de simulação considerando algumas situações de interesse prático com o intuito de comparar as estimativas bayesianas com as frequentistas e também de estudar a sensibilidade dos modelos _a escolha de prioris. Um conjunto de dados da área psicométrica foi analisado para ilustrar o potencial do ferramental desenvolvido. Os resultados indicaram que há ganho ao se considerar modelos que contemplam as observações inflacionadas ao invés de transformá-las a fim de utilizar modelos não inflacionados
Abstract: In the present work we developed Bayesian tools, concerning parameter estimation and diagnostics, for noninflated, zero inflated, one inflated and zero-one inflated beta regression models. Due to the impossibility of obtaining the posterior distributions of interest, analytically, all these tools were developed through MCMC algorithms. For the regression and precision parameters we exploited the using of prior distributions commonly considered in regression models as well as Jeffreys and independence Jeffreys priors. For the parameters related to the discrete components, we considered conjugate prior distributions. We performed simulation studies, considering some situations of practical interest, in order to compare the Bayesian and frequentist estimates as well as to evaluate the sensitivity of the models to the prior choice. A psychometric real data set was analyzed to illustrate the performance of the developed tools. The results indicated that there is an overall improvement in using models that consider the inflated observations compared to transforming these observations in order to use noninflated models
Mestrado
Estatistica
Mestre em Estatística
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MACARO, CHRISTIAN. "Topics on unobserved component detection for time series." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2008. http://hdl.handle.net/2108/691.

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The Impact of Vintage on the Persistence of Gross Domestic Product Shocks. The first chapter of the thesis aims to demonstrate that the data revision process affects the persistence of gross domestic product shocks. The analysis is based on two alternative models, the Fractional Unit Root and the Linear Trend, and it benefits from new semiparametric procedures. The analysis of results seems to suggest that changes in the definition of the output significantly affects the performance of the models which are typically used to study the GDP series. - Seasonality in HighFrequency Data. The second chapter of the thesis aims to study the intradaily seasonal pattern of the Dow Jones volatility. An unobserved component analysis, following the socalled ModelBased approach, is examined in order to separate the seasonal pattern from the remaining long and short term components. The major novelty of the work is to explore the seasonal behavior of the volatility as a stochastic component which evolves over time according to a specific ARMA model. In more detail, high frequency data are used to recover 30minute realized volatility measures. Particular attention has been devoted to checking whether estimates were robust to market microstructure, jumps and outliers. The analysis of results emphasizes that the volatility of the Dow Jones is characterized by a stochastic seasonal component which recalls the “Ushape” pattern. - Bayesian Unobserved Components in Time Series. The third chapter of the thesis aims to present a full Bayesian framework to identify, extract and forecast unobserved components in time series. The major novelty of the approach is the definition of a probabilistic framework to analyze the identification conditions. More precisely, informative prior distributions are assigned to the spectral densities of the unobserved components. This entails a interesting feature: the possibility to analyze more than one decomposition at once by studying the posterior distributions of the unobserved spectra. Particular attention is given to an empirical application where the canonical decomposition of sunspot data is compared with some alternative decompositions. The posterior distributions of the unobserved components are recovered by exploiting some recent developments in the WienerKolmogorov and circular process literature. An empirical application shows how to capture the seasonal component in the volatility of financial high frequency data. The posterior forecasting distributions are finally recovered by exploiting a relationship between spectral densities and linear processes. An empirical application shows how to forecast seasonal adjusted financial time series. Finally, a generalization of the BernsteinDirichlet prior distribution is proposed in order to implement a frequencypass spectral density estimator. - Objective Priors for AR(p) models. The fourth chapter of the thesis aims to derive objective prior distributions for general autoregressive models. The core of the paper is based on the study of the Jeffreys' principle and on a particular adaptation of the “reference” algorithm for dependent data. The analysis of stationarity turns out to be rather complicated for general autoregressive processes. Therefore, two alternative parameterizations which respectively depend on the partial autocorrelation functions (PACFs) and the roots are considered. For the causal and stationary parameter subspace, the PACFs and the roots are always defined within the unit circle. This simplifies the derivation of the prior distributions. Two main results are obtained. The first is a general formula which depends on the PACFs and represents the Jeffreys' prior distribution for the causal subspace. The second result depends on the roots of the process and represents a particular reference prior distribution which is asymptotically independent from the initial conditions and covers the noncausal subspace. Ultimately, simulation results are presented for the autoregressive process of order two.
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Grazian, Clara. "Contributions aux méthodes bayésiennes approchées pour modèles complexes." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED001.

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Récemment, la grande complexité des applications modernes, par exemple dans la génétique, l’informatique, la finance, les sciences du climat, etc. a conduit à la proposition des nouveaux modèles qui peuvent décrire la réalité. Dans ces cas,méthodes MCMC classiques ne parviennent pas à rapprocher la distribution a posteriori, parce qu’ils sont trop lents pour étudier le space complet du paramètre. Nouveaux algorithmes ont été proposés pour gérer ces situations, où la fonction de vraisemblance est indisponible. Nous allons étudier nombreuses caractéristiques des modèles complexes: comment éliminer les paramètres de nuisance de l’analyse et faire inférence sur les quantités d’intérêt,dans un cadre bayésienne et non bayésienne et comment construire une distribution a priori de référence
Recently, the great complexity of modern applications, for instance in genetics,computer science, finance, climatic science etc., has led to the proposal of newmodels which may realistically describe the reality. In these cases, classical MCMCmethods fail to approximate the posterior distribution, because they are too slow toinvestigate the full parameter space. New algorithms have been proposed to handlethese situations, where the likelihood function is unavailable. We will investigatemany features of complex models: how to eliminate the nuisance parameters fromthe analysis and make inference on key quantities of interest, both in a Bayesianand not Bayesian setting, and how to build a reference prior
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Wang, Guojun. "Some Bayesian Methods in the Estimation of Parameters in the Measurement Error Models and Crossover Trial." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1076852153.

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Li, Zhonggai. "Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28121.

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This dissertation consists of four independent but related parts, each in a Chapter. The first part is an introductory. It serves as the background introduction and offer preparations for later parts. The second part discusses two population multivariate normal distributions with common covariance matrix. The goal for this part is to derive objective/non-informative priors for the parameterizations and use these priors to build up constructive random posteriors of the Kullback-Liebler (KL) divergence of the two multivariate normal populations, which is proportional to the distance between the two means, weighted by the common precision matrix. We use the Cholesky decomposition for re-parameterization of the precision matrix. The KL divergence is a true distance measurement for divergence between the two multivariate normal populations with common covariance matrix. Frequentist properties of the Bayesian procedure using these objective priors are studied through analytical and numerical tools. The third part considers the star-shape Gaussian graphical model, which is a special case of undirected Gaussian graphical models. It is a multivariate normal distribution where the variables are grouped into one "global" group of variable set and several "local" groups of variable set. When conditioned on the global variable set, the local variable sets are independent of each other. We adopt the Cholesky decomposition for re-parametrization of precision matrix and derive Jeffreys' prior, reference prior, and invariant priors for new parameterizations. The frequentist properties of the Bayesian procedure using these objective priors are also studied. The last part concentrates on the discussion of objective Bayesian analysis for partial correlation coefficient and its application to multivariate Gaussian models.
Ph. D.
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Heard, Astrid. "APPLICATION OF STATISTICAL METHODS IN RISK AND RELIABILITY." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2602.

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The dissertation considers construction of confidence intervals for a cumulative distribution function F(z) and its inverse at some fixed points z and u on the basis of an i.i.d. sample where the sample size is relatively small. The sample is modeled as having the flexible Generalized Gamma distribution with all three parameters being unknown. This approach can be viewed as an alternative to nonparametric techniques which do not specify distribution of X and lead to less efficient procedures. The confidence intervals are constructed by objective Bayesian methods and use the Jeffreys noninformative prior. Performance of the resulting confidence intervals is studied via Monte Carlo simulations and compared to the performance of nonparametric confidence intervals based on binomial proportion. In addition, techniques for change point detection are analyzed and further evaluated via Monte Carlo simulations. The effect of a change point on the interval estimators is studied both analytically and via Monte Carlo simulations.
Ph.D.
Department of Mathematics
Arts and Sciences
Mathematics
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Guo, Yixuan. "Bayesian Model Selection for Poisson and Related Models." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310177.

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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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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.
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Books on the topic "Jeffreys prior"

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Bauman, Thomas. Holding the Stroll. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252038365.003.0005.

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This chapter focuses on the Pekin Theater's vaudeville shows that pervaded The Stroll at the time. When the curtain had rung down on the final performance of The Husband, Robert T. Motts dismissed his stock company. J. Ed. Green resigned and decided to go into business by forming the Chester Amusement Company with Marion Brooks and A. W. Johnson. At the Pekin, Motts further strengthened and distinguished the musical profile of the house by reinstating a kind of music that had first drawn patrons there in 1904: concerts, motion pictures, and vaudeville acts featuring “society” performers such as Marie Burton. This chapter also considers Motts's invitation to the Howard Stock Company to perform at the Pekin and concludes with a discussion of the special event that broke all prior attendance records at the theater—the match between heavyweight champion Jack Johnson and challenger Jim Jeffries.
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Book chapters on the topic "Jeffreys prior"

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Firth, David. "Bias reduction, the Jeffreys prior and GLIM." In Advances in GLIM and Statistical Modelling, 91–100. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_15.

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Dasgupta, Ratan. "Coconut Plant Growth, Mahalanobis Distance, and Jeffreys’ Prior." In Growth Curve Models and Applications, 115–25. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63886-7_5.

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Yanagimoto, Takemi, and Toshio Ohnishi. "A Characterization of Jeffreys’ Prior with Its Implications to Likelihood Inference." In Pioneering Works on Distribution Theory, 103–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9663-6_6.

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Grazian, Clara, and Christian P. Robert. "Jeffreys’ Priors for Mixture Estimation." In Springer Proceedings in Mathematics & Statistics, 37–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16238-6_4.

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Firth, D. "Generalized Linear Models and Jeffreys Priors: An Iterative Weighted Least-Squares Approach." In Computational Statistics, 553–57. Heidelberg: Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-662-26811-7_76.

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Févotte, Cédric, and Simon J. Godsill. "Blind Separation of Sparse Sources Using Jeffrey’s Inverse Prior and the EM Algorithm." In Independent Component Analysis and Blind Signal Separation, 593–600. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_74.

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Eifert, Erin P., Kalanka P. Jayalath, and Raj S. Chhikara. "Survival Analysis for the Inverse Gaussian Distribution: Natural Conjugate and Jeffrey’s Priors." In Emerging Topics in Statistics and Biostatistics, 279–98. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-88658-5_13.

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Tao, Kaiyuan, Chuang Wang, Junli Xia, Yanfang Wang, and Weike Du. "Michael Jeffrey Jordan v. Trademark Review and Adjudication Board of the State Administration for Industry and Commerce of the People’s Republic of China & Jordan Sports Co., Ltd. (Administrative Disputes over Trademark)—Right to One’s Name May Constitute “Prior Right” Protected by Trademark Law." In Library of Selected Cases from the Chinese Court, 17–31. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0342-9_2.

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Du, Weike, and Xian Tang. "Michael Jeffrey Jordan v. Trademark Review and Adjudication Board of the State Administration for Industry and Commerce of the People's Republic of China and Qiaodan Sports Products Co., Ltd. [Administrative Dispute over (Graphics) Trademark Infringement]: Requirements for Protecting the Prior Right of Image in Trademark Administrative Cases." In Library of Selected Cases from the Chinese Court, 315–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9136-5_32.

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"Appendix D Jeffreys Prior." In Practical Applications of Bayesian Reliability, 295–97. Chichester, UK: John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119287995.app4.

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Conference papers on the topic "Jeffreys prior"

1

Motomura, Yoichi. "Jeffreys' prior for layered neural networks." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Steven K. Rogers and Dennis W. Ruck. SPIE, 1995. http://dx.doi.org/10.1117/12.205194.

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Nguyen, Tam, Raviv Raich, and Phung Lai. "Jeffreys prior regularization for logistic regression." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551820.

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