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1دورية أكاديمية
المؤلفون: Felix Abramovich, Claudia Angelini, Daniela Canditiis
الوصف: Bayes Factor, Bayes model, Bayesian paradox, Besov spaces, Minimax rates, Nonparametric regression, Point estimation, Posterior mean, Posterior median, Wavelets
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2دورية أكاديمية
المؤلفون: Felix Abramovich, Umberto Amato, Claudia Angelini
الوصف: We investigate the asymptotic optimality of several Bayesian wavelet estimators, namely, posterior mean, posterior median and Bayes Factor, where the prior imposed on wavelet coefficients is a mixture of a mass function at zero and a Gaussian density. We show that in terms of the mean squared error, for the properly chosen hyperparameters of the prior, all the three resulting Bayesian wavelet estimators achieve optimal minimax rates within any prescribed Besov space for p ≥ 2. For 1 ≤ p
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3دورية أكاديمية
المؤلفون: Claudia Angelini, Daniela De Canditiis, Marianna Pensky
الوصف: This paper presents a new Bayesian, infinite mixture model based, clustering approach, specifically designed for time-course microarray data. The problem is to group together genes which have “similar” expression profiles, given the set of noisy measurements of their expression levels over a specific time interval. In order to capture temporal variations of each curve, a non-parametric regression approach is used. Each expression profile is expanded over a set of basis functions and the sets of coefficients of each curve are subsequently modeled through a Bayesian infinite mixture of Gaussian distributions. Therefore, the task of finding clusters of genes with similar expression profiles is then reduced to the problem of grouping together genes whose coefficients are sampled from the same distribution in the mixture. Dirichlet processes prior is naturally employed in such kinds of models, since it allows one to deal automatically with the uncertainty about the number of clusters. The posterior inference is carried out by a split and merge MCMC sampling scheme which integrates out parameters of the component distributions and updates only the latent vector of the cluster membership. The final configuration is obtained via the maximum a posteriori estimator. The performance of the method is studied using synthetic and real microarray data and is compared with the performances of competitive techniques.
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4دورية أكاديمية
المؤلفون: Nicola G Criscuolo, Claudia Angelini
الوصف: Population genetics focuses on the analysis of genetic differences within and between-group of individuals and the inference of the populations’ structure. These analyses are usually carried out using Bayesian clustering or maximum likelihood estimation algorithms that assign individuals to a given population depending on specific genetic patterns. Although several tools were developed to perform population genetics analysis, their standard graphical outputs may not be sufficiently informative for users lacking interactivity and complete information. StructuRly aims to resolve this problem by offering a complete environment for population analysis. In particular, StructuRly combines the statistical power of the R language with the friendly interfaces implemented using the shiny libraries to provide a novel tool for performing population clustering, evaluating several genetic indexes, and comparing results. Moreover, graphical representations are interactive and can be easily personalized. StructuRly is available either as R package on GitHub, with detailed information for its installation and use and as shinyapps.io servers for those users who are not familiar with R and the RStudio IDE. The application has been tested on Linux, macOS and Windows operative systems and can be launched as a shiny app in every web browser.
العلاقة: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229330Test; https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0229330&type=printableTest
الإتاحة: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229330Test
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0229330&type=printableTest
