1,720,964 research outputs found
Approssimazioni asimmetriche delle distribuzioni a posteriori
No full textIn Bayesian statistics, routinely implemented deterministic approximations of posterior distributions typically rely on symmetric densities, often taken to be Gaussian. Such a choice facilitates optimization and inference, but may compromise the quality of the overall approximation. In fact, even in simple parametric models, the posterior distribution can displays substantial asymmetries that yield major bias and reduced accuracy when considering symmetric approximations. Recent research has moved toward more flexible classes of approximating densities incorporating skewness. However, current solutions are model specific, lack general supporting theory and usually increase the computational challenges and complexity of the optimization problem.
This thesis aims to fill such a gap by developing a general, and theoretically supported, family of skew-symmetric approximations. To accomplish this goal, Chapter 1 demonstrates that in the idealized framework where the true data generating mechanism is known, the posterior distribution converges, in an appropriate sense, to a specific sequence of skew-symmetric distributions at a rate that is faster than the classical Gaussian one derived under the Bernstein-Von Mises theorem. In Chapter 2, these findings further motivate the development of practical plug-in versions that, besides enjoying the same theoretical guarantees, can approximate the posterior distribution in real-world scenarios. The approximations developed in the first two chapters are derived by exploiting asymptotic arguments. Chapter 3 offers a different perspective by introducing a general and provably optimal strategy to perturb any off-the-shelf symmetric approximation of a generic posterior distribution. Such a novel perturbation is derived without additional optimization steps and yields a similarly-tractable approximation within the class of skew-symmetric densities that provably improve
Skewed Bernstein-von Mises theorem and skew-modal approximations
No full textGaussian deterministic approximations are routinely employed in Bayesian statistics to ease inference when the target posterior is intractable. Although these approximations are justified, in asymptotic regimes, by Bernstein-von Mises type results, in practice the expected Gaussian behavior might poorly represent the actual shape of the target posterior, thus affecting approximation accuracy. Motivated by these considerations, we derive an improved class of closed-form and valid deterministic approximations of posterior distributions that arise from a novel treatment of a third-order version of the Laplace method yielding approximations within a tractable family of skew-symmetric distributions. Under general assumptions accounting for misspecified models and non-i.i.d. settings, this family of approximations is shown to have a total variation distance from the target posterior whose convergence rate improves by at least one order of magnitude the one achieved by the Gaussian from the classical Bernstein-von Mises theorem. Specializing such a result to the case of regular parametric models shows that the same accuracy improvement can be also established for the posterior expectation of polynomially bounded functions. Unlike available higher-order approximations based on, for example, Edgeworth expansions, our results prove that it is possible to derive closed-form and valid densities which provide a more accurate, yet similarly-tractable, alternative to Gaussian approximations of the target posterior, while inheriting its limiting frequentist properties. We strengthen these arguments by developing a practical skew-modal approximation for both joint and marginal posteriors which preserves the guarantees of its theoretical counterpart by replacing the unknown model parameters with the corresponding maximum a posteriori estimate. Simulation studies and real-data applications confirm that our theoretical results closely match the empirical gains observed in practice
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Improved and computationally stable estimation of relative risk regression with one binary exposure
No full text: In medical statistics, when the effect of a binary risk factor on a binary response is of interest, relative risk is often the preferred measure due to its direct interpretation. However, statistical inference on this quantity is not as straightforward as for other measures of association, especially when further explanatory variables have to be taken into account. Starting from a review of available methods for inference on relative risk, this paper deals with small and moderate sample size settings for which we show that classical approaches can be problematic. For this reason, we propose the use of improved estimation procedures, aiming at mean or median bias reduction of the maximum likelihood estimator. In particular, these methods are developed for a new alternative specification of a model recently proposed by Richardson et al, where higher computational stability of the estimation methods is achieved. A real-data example and extensive simulation studies show that the proposed methods perform remarkably better than the standard ones
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
- …
