76 research outputs found
On the Independence Jeffreys prior for skew--symmetric models with applications
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 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
Approximate Bayesian conditional copulas
Copula models are flexible tools to represent complex structures of dependence for multivariate random variables. According to Sklar's theorem, any multidimensional absolutely continuous distribution function can be uniquely represented as a copula, i.e. a joint cumulative distribution function on the unit hypercube with uniform marginals, which captures the dependence structure among the vector components. In real data applications, the interest of the analyses often lies on specific functionals of the dependence, which quantify aspects of it in a few numerical values. A broad literature exists on such functionals, however extensions to include covariates are still limited. This is mainly due to the lack of unbiased estimators of the conditional copula, especially when one does not have enough information to select the copula model. Several Bayesian methods to approximate the posterior distribution of functionals of the dependence varying according covariates are presented and compared; the main advantage of the investigated methods is that they use nonparametric models, avoiding the selection of the copula, which is usually a delicate aspect of copula modelling. These methods are compared in simulation studies and in two realistic applications, from civil engineering and astrophysics. (C) 2022 Elsevier B.V. All rights reserved
Generalized Bayesian Record Linkage and Regression with Exact Error Propagation
Record linkage (de-duplication or entity resolution) is the process of
merging noisy databases to remove duplicate entities. While record linkage
removes duplicate entities from such databases, the downstream task is any
inferential, predictive, or post-linkage task on the linked data. One goal of
the downstream task is obtaining a larger reference data set, allowing one to
perform more accurate statistical analyses. In addition, there is inherent
record linkage uncertainty passed to the downstream task. Motivated by the
above, we propose a generalized Bayesian record linkage method and consider
multiple regression analysis as the downstream task. Records are linked via a
random partition model, which allows for a wide class to be considered. In
addition, we jointly model the record linkage and downstream task, which allows
one to account for the record linkage uncertainty exactly. Moreover, one is
able to generate a feedback propagation mechanism of the information from the
proposed Bayesian record linkage model into the downstream task. This feedback
effect is essential to eliminate potential biases that can jeopardize resulting
downstream task. We apply our methodology to multiple linear regression, and
illustrate empirically that the "feedback effect" is able to improve the
performance of record linkage.Comment: 18 pages, 5 figure
An Objective Bayesian Analysis of dichotomous sensitive data
We consider a dichotomous population in which every
person belongs either to a sensitive group or to the non
sensitive complement . The object of interest is to
estimate the population proportion of individuals who are members
of . We refer to a randomized response model proposed by Huang
(2004), where also another parameter is present, namely the
probability that a respondent truthfully states that he/she
belongs to in a direct response survey. In the paper the
posterior distribution of the parameters under the joint Jeffreys
and Reference prior is derived. The properties of the
noninformative priors are investigated through the frequentist
coverage probabilities of posterior quantiles
Approximate Bayesian methods for multivariate and conditional copulae
We describe a simple method for making inference on a functional of a multivariate distribution. The method is based on a copula representation of the multivariate distribution, where copula is a flexible probabilistic tool that allows the researcher to model the joint distribution of a random vector in two separate steps: the marginal distributions and a copula function which captures the dependence structure among the vector components. The method is also based on the properties of an approximate BayesianMonteCarlo algorithm, where the proposed values of the functional of interest areweighted in terms of their empirical likelihood. This method is particularly useful when the likelihood function associated with theworking model is too costly to evaluate or when the working model is only partially specified
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