Journal ArticleTest · June 1, 2023
We explore the use of penalized complexity (PC) priors for assessing the dependence structure in a multivariate distribution F, with a particular emphasis on the bivariate case. We use the copula representation of F and derive the PC prior for the paramete ...
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Journal ArticleInternational Statistical Review · April 1, 2022
Cyber security is an important concern for all individuals, organisations and governments globally. Cyber attacks have become more sophisticated, frequent and dangerous than ever, and traditional anomaly detection methods have been proved to be less effect ...
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Journal ArticleComputational Statistics and Data Analysis · April 1, 2022
A new look at the use of improper priors in Bayes factors for model comparison is presented. As is well known, in such a case, the Bayes factor is only defined up to an arbitrary constant. Most current methods overcome the problem by using part of the samp ...
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Journal ArticleAustralian and New Zealand Journal of Statistics · December 1, 2020
Gaussian graphical models play an important role in various areas such as genetics, finance, statistical physics and others. They are a powerful modelling tool, which allows one to describe the relationships among the variables of interest. From the Bayesi ...
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Journal ArticleStatistical Methods and Applications · June 1, 2020
Two-piece location-scale models are used for modeling data presenting departures from symmetry. In this paper, we propose an objective Bayesian methodology for the tail parameter of two particular distributions of the above family: the skewed exponential p ...
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Journal ArticleStatistics and Probability Letters · March 1, 2020
We introduce a prior distribution for the number of components of a mixture model. The prior considers the worth of each possible mixture, measured by a loss function with two components: one measures the loss in information in choosing the wrong mixture a ...
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Journal ArticleBayesian Analysis · January 1, 2020
Objective prior distributions represent an important tool that allows one to have the advantages of using a Bayesian framework even when information about the parameters of a model is not available. The usual objective approaches work off the chosen statis ...
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Journal ArticleBayesian Analysis · January 1, 2020
In this work we propose a novel model prior for variable selection in linear regression. The idea is to determine the prior mass by considering the worth of each of the regression models, given the number of possible covariates under consideration. The wor ...
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Journal ArticleComputational Statistics and Data Analysis · January 1, 2019
A loss-based approach to change point analysis is proposed. In particular, the problem is looked from two perspectives. The first focuses on the definition of a prior when the number of change points is known a priori. The second contribution aims to estim ...
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Journal ArticleComputational Statistics and Data Analysis · August 1, 2018
An objective Bayesian approach to estimate the number of degrees of freedom (ν) for the multivariate t distribution and for the t-copula, when the parameter is considered discrete, is proposed. Inference on this parameter has been problematic for the multi ...
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Journal ArticleComputational Statistics · March 1, 2018
The Yule–Simon distribution is usually employed in the analysis of frequency data. As the Bayesian literature, so far, has ignored this distribution, here we show the derivation of two objective priors for the parameter of the Yule–Simon distribution. In p ...
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Journal ArticleCommunications in Statistics - Theory and Methods · December 17, 2017
This paper is concerned with the well known Jeffreys–Lindley paradox. In a Bayesian set up, the so-called paradox arises when a point null hypothesis is tested and an objective prior is sought for the alternative hypothesis. In particular, the posterior fo ...
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Journal ArticleJournal of Statistical Computation and Simulation · April 13, 2017
The Yule–Simon distribution has been out of the radar of the Bayesian community, so far. In this note, we propose an explicit Gibbs sampling scheme when a Gamma prior is chosen for the shape parameter. The performance of the algorithm is illustrated with s ...
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Journal ArticleTest · March 1, 2017
In this paper, we discuss a method to define prior distributions for the threshold of a generalised Pareto distribution, in particular when its applications are directed to heavy-tailed data. We propose to assign prior probabilities to the order statistics ...
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Journal ArticleApplied Stochastic Models in Business and Industry · March 1, 2017
Insurance risks data typically exhibit skewed behaviour. In this paper, we propose a Bayesian approach to capture the main features of these data sets. This work extends a methodology recently introduced in the literature by considering an extra parameter ...
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Journal ArticleScandinavian Journal of Statistics · December 1, 2015
We discuss the problem of selecting among alternative parametric models within the Bayesian framework. For model selection problems, which involve non-nested models, the common objective choice of a prior on the model space is the uniform distribution. The ...
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Journal ArticleJournal of the American Statistical Association · July 3, 2015
We present a novel approach to constructing objective prior distributions for discrete parameter spaces. These types of parameter spaces are particularly problematic, as it appears that common objective procedures to design prior distributions are problem ...
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Journal ArticleBayesian Analysis · January 1, 2014
In this paper, we construct an objective prior for the degrees of freedom of a t distribution, when the parameter is taken to be discrete. This parameter is typically problematic to estimate and a problem in objective Bayesian inference since improper prio ...
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