Página 1 dos resultados de 372 itens digitais encontrados em 0.003 segundos

## Digestão de diferentes carboidratos em Nausitora fusticula (Jeffreys, 1860) (Bivalvia, Teredinidae); Digestion of carbohydrats in Nausitora fusticula (Jeffreys, 1860) (Molusca, Bivalvia)

Cruz, Rafael Cancian Gomes da
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
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## Bayesian estimation of generalized exponential distribution under noninformative priors

Moala, Fernando Antonio; Achcar, Jorge Alberto; Damasceno Tomazella, Vera Lucia; Stern, JM; Lauretto, MD; Polpo, A; Diniz, MA
Fonte: Amer Inst Physics Publicador: Amer Inst Physics
Tipo: Conferência ou Objeto de Conferência Formato: 230-242
ENG
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The generalized exponential distribution, proposed by Gupta and Kundu (1999), is a good alternative to standard lifetime distributions as exponential, Weibull or gamma. Several authors have considered the problem of Bayesian estimation of the parameters of generalized exponential distribution, assuming independent gamma priors and other informative priors. In this paper, we consider a Bayesian analysis of the generalized exponential distribution by assuming the conventional non-informative prior distributions, as Jeffreys and reference prior, to estimate the parameters. These priors are compared with independent gamma priors for both parameters. The comparison is carried out by examining the frequentist coverage probabilities of Bayesian credible intervals. We shown that maximal data information prior implies in an improper posterior distribution for the parameters of a generalized exponential distribution. It is also shown that the choice of a parameter of interest is very important for the reference prior. The different choices lead to different reference priors in this case. Numerical inference is illustrated for the parameters by considering data set of different sizes and using MCMC (Markov Chain Monte Carlo) methods.

## A bayesian analysis for the parameters of the exponential-logarithmic distribution

Moala, Fernando A.; Garcia, Lívia M.
Tipo: Artigo de Revista Científica Formato: 282-291
ENG
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The exponential-logarithmic is a new lifetime distribution with decreasing failure rate and interesting applications in the biological and engineering sciences. Thus, a Bayesian analysis of the parameters would be desirable. Bayesian estimation requires the selection of prior distributions for all parameters of the model. In this case, researchers usually seek to choose a prior that has little information on the parameters, allowing the data to be very informative relative to the prior information. Assuming some noninformative prior distributions, we present a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Jeffreys prior is derived for the parameters of exponential-logarithmic distribution and compared with other common priors such as beta, gamma, and uniform distributions. In this article, we show through a simulation study that the maximum likelihood estimate may not exist except under restrictive conditions. In addition, the posterior density is sometimes bimodal when an improper prior density is used. © 2013 Copyright Taylor and Francis Group, LLC.

## Bayesian inference for two-parameter gamma distribution assuming different noninformative priors

Moala, Fernando Antonio; Ramos, Pedro Luiz; Achcar, Jorge Alberto
Tipo: Artigo de Revista Científica Formato: 321-338
ENG
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In this paper distinct prior distributions are derived in a Bayesian inference of the two-parameters Gamma distribution. Noniformative priors, such as Jeffreys, reference, MDIP, Tibshirani and an innovative prior based on the copula approach are investigated. We show that the maximal data information prior provides in an improper posterior density and that the different choices of the parameter of interest lead to different reference priors in this case. Based on the simulated data sets, the Bayesian estimates and credible intervals for the unknown parameters are computed and the performance of the prior distributions are evaluated. The Bayesian analysis is conducted using the Markov Chain Monte Carlo (MCMC) methods to generate samples from the posterior distributions under the above priors.

## Inferência bayesiana em modelos de regressão beta e beta inflacionados; Bayesian inference in beta and inflated beta regression models

Danilo Covaes Nogarotto
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
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## Dr. Jeffreys on Medical Clubs

Jeffreys, Thos.
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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## On conjugate families and Jeffreys priors for von Mises–Fisher distributions

Hornik, Kurt; Grün, Bettina
Tipo: Artigo de Revista Científica
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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 (matrix-valued) von Mises–Fisher distributions on Stiefel manifolds.

## The man behind the DNA fingerprints: an interview with Professor Sir Alec Jeffreys

Jeffreys, Alec J
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
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In this interview we talk with Professor Sir Alec Jeffreys about DNA fingerprinting, his wider scientific career, and the past, present and future of forensic DNA applications.

## I Got More Data, My Model is More Refined, but My Estimator is Getting Worse! Am I Just Dumb?

Meng, Xiao-Li; Xie, Xianchao
Fonte: Informa UK (Taylor & Francis) Publicador: Informa UK (Taylor & Francis)
Tipo: Artigo de Revista Científica
EN_US
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Possibly, but more likely you are merely a victim of conventional wisdom. More data or better models by no means guarantee better estimators (e.g., with a smaller mean squared error), when you are not following probabilistically principled methods such as MLE (for large samples) or Bayesian approaches. Estimating equations are particularly vulnerable in this regard, almost a necessary price for their robustness. These points will be demonstrated via common tasks of estimating regression parameters and correlations, under simple models such as bivariate normal and ARCH(1). Some general strategies for detecting and avoiding such pitfalls are suggested, including checking for self-efficiency (Meng, 1994; Statistical Science) and adopting a guiding working model. Using the example of estimating the autocorrelation ( ho) under a stationary AR(1) model, we also demonstrate the interaction between model assumptions and observation structures in seeking additional information, as the sampling interval (s) increases. Furthermore, for a given sample size, the optimal s for minimizing the asymptotic variance of (hat{ ho}_{MLE})is (s = 1) if and only if ( ho^2 ≤ 1/3); beyond that region the optimal s increases at the rate of (log ^{−1}( ho^{−2})) as ( ho) approaches a unit root...

## Rural clinician opinion on being a preceptor

Shannon, S.; Walker-Jeffreys, M.; Newbury, J.; Cayetano, T.; Brown, K.; Petkov, J.
Fonte: Deakin University Publicador: Deakin University
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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Shannon, SJ; Walker-Jeffreys, M; Newbury, JW; Cayetano, T; Brown, K; Petkov, J; Copyright © Susan Shannon, May Walker-Jeffreys, Jonathan Newbury, Teofilonestor Cayetano, Kate Brown, John Petkov 2006 A licence to publish this material has been given to ARHEN, http://www.rrh.org.au

## Nesbitt, ed., Byzantine Authors (Elizabeth Jeffreys)

Elizabeth Jeffreys
Fonte: The Medieval Review Publicador: The Medieval Review
Tipo: Revisão
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Elizabeth Jeffreys, University of Oxford, elizabeth.jeffreys@modern-languages.oxford.ac.uk

## "Testes de hipótese e critério bayesiano de seleção de modelos para séries temporais com raiz unitária" ; "Hypothesis testing and bayesian model selection for time series with a unit root"

Silva, Ricardo Gonçalves da
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Relevância na Pesquisa
27.24%

## Local immobilization of particles in mass transfer described by the equation of the Jeffreys type

Rukolaine, Sergey A.; Samsonov, Alexander M.
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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We consider the equation of the Jeffreys type as the basic one in three different models of mass transfer, namely, the Jeffreys type and two-phase models, and the $D_1$ approximation to the linear Boltzmann equation. We study two classic 1 + 1D problems in the framework of each model. The first problem is the transfer of a substance initially confined in a point. The second problem is the transfer of a substance from a stationary point source. We calculate the mean-square displacement (MSD) for the solutions of the first problem. The temporal behaviour of the MSD in the framework of the first and third models is found to be the same as that in the Brownian motion described by the standard Langevin equation. Besides, we find a remarkable phenomenon when a portion of the substance does not move.; Comment: 15 pages, 7 figures

## On the Independence Jeffreys prior for skew--symmetric models with applications

Rubio, F. J.; Liseo, B.
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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We study the Jeffreys prior of the skewness parameter of a general class of scalar skew--symmetric models. It is shown that this prior is symmetric about 0, proper, and with tails $O(\lambda^{-3/2})$ under mild regularity conditions. We also calculate the independence Jeffreys prior for the case with unknown location and scale parameters. Sufficient conditions for the existence of the corresponding posterior distribution are investigated for the case when the sampling model belongs to the family of skew--symmetric scale mixtures of normal distributions. The usefulness of these results is illustrated using the skew--logistic model and two applications with real data.

## The Jeffreys-Lindley Paradox and Discovery Criteria in High Energy Physics

Cousins, Robert D.
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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The Jeffreys-Lindley paradox displays how the use of a p-value (or number of standard deviations z) in a frequentist hypothesis test can lead to an inference that is radically different from that of a Bayesian hypothesis test in the form advocated by Harold Jeffreys in the 1930s and common today. The setting is the test of a well-specified null hypothesis (such as the Standard Model of elementary particle physics, possibly with "nuisance parameters") versus a composite alternative (such as the Standard Model plus a new force of nature of unknown strength). The p-value, as well as the ratio of the likelihood under the null hypothesis to the maximized likelihood under the alternative, can strongly disfavor the null hypothesis, while the Bayesian posterior probability for the null hypothesis can be arbitrarily large. The academic statistics literature contains many impassioned comments on this paradox, yet there is no consensus either on its relevance to scientific communication or on its correct resolution. The paradox is quite relevant to frontier research in high energy physics. This paper is an attempt to explain the situation to both physicists and statisticians, in the hope that further progress can be made.; Comment: v4: Continued editing for clarity. Figure added. v5: Minor fixes to biblio. Same as published version except for minor copy-edits...

## Bayesian estimation of a bivariate copula using the Jeffreys prior

Guillotte, Simon; Perron, François
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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A bivariate distribution with continuous margins can be uniquely decomposed via a copula and its marginal distributions. We consider the problem of estimating the copula function and adopt a Bayesian approach. On the space of copula functions, we construct a finite-dimensional approximation subspace that is parametrized by a doubly stochastic matrix. A major problem here is the selection of a prior distribution on the space of doubly stochastic matrices also known as the Birkhoff polytope. The main contributions of this paper are the derivation of a simple formula for the Jeffreys prior and showing that it is proper. It is known in the literature that for a complex problem like the one treated here, the above results are difficult to obtain. The Bayes estimator resulting from the Jeffreys prior is then evaluated numerically via Markov chain Monte Carlo methodology. A rather extensive simulation experiment is carried out. In many cases, the results favour the Bayes estimator over frequentist estimators such as the standard kernel estimator and Deheuvels' estimator in terms of mean integrated squared error.; Comment: Published in at http://dx.doi.org/10.3150/10-BEJ345 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

## On posterior propriety for the Student-$t$ linear regression model under Jeffreys priors

Vallejos, Catalina A.; Steel, Mark F. J.
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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Regression models with fat-tailed error terms are an increasingly popular choice to obtain more robust inference to the presence of outlying observations. This article focuses on Bayesian inference for the Student-$t$ linear regression model under objective priors that are based on the Jeffreys rule. Posterior propriety results presented in Fonseca et al. (2008) are revisited and corrected. In particular, it is shown that the standard Jeffreys-rule prior precludes the existence of a proper posterior distribution.; Comment: minor editorial changes in this version

## Jeffreys priors for mixture estimation

Grazian, Clara; Robert, Christian
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
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While Jeffreys priors usually are well-defined for the parameters of mixtures of distributions, they are not available in closed form. Furthermore, they often are improper priors. Hence, they have never been used to draw inference on the mixture parameters. We study in this paper the implementation and the properties of Jeffreys priors in several mixture settings, show that the associated posterior distributions most often are improper, and then propose a noninformative alternative for the analysis of mixtures.

## On the viscoelastic characterization of the Jeffreys-Lomnitz law of creep

In 1958 Jeffreys proposed a power law of creep, generalizing the logarithmic law earlier introduced by Lomnitz, to broaden the geophysical applications to fluid-like materials including igneous rocks. This generalized law, however, can be applied also to solid-like viscoelastic materials. We revisit the Jeffreys-Lomnitz law of creep by allowing its power law exponent $\alpha$, usually limited to the range [0,1] to all negative values. This is consistent with the linear theory of viscoelasticity because the creep function still remains a Bernstein function, that is positive with a completely monotonic derivative, with a related spectrum of retardation times. The complete range $\alpha \le 1$ yields a continuous transition from a Hooke elastic solid with no creep ($\alpha \to -\infty$) to a Maxwell fluid with linear creep ($\alpha=1$) passing through the Lomnitz viscoelastic body with logarithmic creep ($\alpha=0$), which separates solid-like from fluid-like behaviors. Furthermore, we numerically compute the relaxation modulus and provide the analytical expression of the spectrum of retardation times corresponding to the Jeffreys-Lomnitz creep law extended to all $\alpha \le 1$.; Comment: 23 pages, 3 figures (5 files .ps)
Due to the success of the bag-of-word modeling paradigm, clustering histograms has become an important ingredient of modern information processing. Clustering histograms can be performed using the celebrated $k$-means centroid-based algorithm. From the viewpoint of applications, it is usually required to deal with symmetric distances. In this letter, we consider the Jeffreys divergence that symmetrizes the Kullback-Leibler divergence, and investigate the computation of Jeffreys centroids. We first prove that the Jeffreys centroid can be expressed analytically using the Lambert $W$ function for positive histograms. We then show how to obtain a fast guaranteed approximation when dealing with frequency histograms. Finally, we conclude with some remarks on the $k$-means histogram clustering.; Comment: 17 pages, 1 figure, source code in R