1,720,957 research outputs found
Methodological and Computational Advances for High–Dimensional Bayesian Regression with Binary and Categorical Responses
Full text linkProbit and logistic regressions are among the most popular and well-established formulations to model binary observations, thanks to their plain structure and high interpretability. Despite their simplicity, their use poses non-trivial hindrances to the inferential procedure, particularly from a computational perspective and in high-dimensional scenarios. This still motivates thriving active research for probit, logit, and a number of their generalizations, especially within the Bayesian community. Conjugacy results for standard probit regression under normal and unified skew-normal (SUN) priors appeared only recently in the literature. Such findings were rapidly extended to different generalizations of probit regression, including multinomial probit, dynamic multivariate probit and skewed Gaussian processes among others. Nonetheless, these recent developments focus on specific subclasses of models, which can all be regarded as instances of a potentially broader family of formulations, that rely on partially or fully discretized Gaussian latent utilities. As such, we develop a unified comprehensive framework that encompasses all the above constructions and many others, such as tobit regression and its extensions, for which conjugacy results are yet missing. We show that the SUN family of distribution is conjugate for all models within the broad class considered, which notably encompasses all formulations relying on likelihoods given by the product of multivariate Gaussian densities and cumulative distributions, evaluated at a linear combination of the parameter of interest. Such a unifying framework is practically and conceptually useful for studying general theoretical properties and developing future extensions. This includes new avenues for improved posterior inference exploiting i.i.d. samplers from the exact SUN posteriors and recent accurate and scalable variational Bayes (VB) approximations and expectation-propagation, for which we derive a novel efficient implementation.
Along a parallel research line, we focus on binary regression under logit mapping, for which computations in high dimensions still pose open challenges. To overcome such difficulties, several contributions focus on solving iteratively a series of surrogate problems, entailing the sequential refinement of tangent lower bounds for the logistic log-likelihoods. For instance, tractable quadratic minorizers can be exploited to obtain maximum likelihood (ML) and maximum a posteriori estimates via minorize-maximize and expectation-maximization schemes, with desirable convergence guarantees. Likewise, quadratic surrogates can be used to construct Gaussian approximations of the posterior distribution in mean-field VB routines, which might however suffer from low accuracy in high dimensions. This issue can be mitigated by resorting to more flexible but involved piece-wise quadratic bounds, that however are typically defined in an implicit way and entail reduced tractability as the number of pieces increases. For this reason, we derive a novel tangent minorizer for logistic log-likelihoods, that combines the quadratic term with a single piece-wise linear contribution per each observation, proportional to the absolute value of the corresponding linear predictor. The proposed bound is guaranteed to improve the accuracy over the sharpest among quadratic minorizers, while minimizing the reduction in tractability compared to general piece-wise quadratic bounds. As opposed to the latter, its explicit analytical expression allows to simplify computations by exploiting a renowned scale-mixture representation of Laplace random variables. We investigate the benefit of the proposed methodology both in the context of penalized ML estimation, where it leads to a faster convergence rate of the optimization procedure, and of VB approximation, as the resulting accuracy improvement over mean-field strategies can be substantial in skewed and high-dimensional scenarios
Efficient computation of predictive probabilities in probit models via expectation propagation
No full textEfficient expectation propagation for posterior approximation in high-dimensional probit models
No full textBayesian Conjugacy in Probit, Tobit, Multinomial Probit and Extensions: A Review and New Results
No full textA broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including classical Gaussian response settings, this class also encompasses probit, multinomial probit and tobit regression, among others, thereby yielding one of the most widely-implemented families of models in routine applications. The relevance of such representations has stimulated decades of research in the Bayesian field, mostly motivated by the fact that, unlike for Gaussian linear regression, the posterior distribution induced by such models does not seem to belong to a known class, under the commonly-assumed Gaussian priors for the coefficients. This has motivated several solutions for posterior inference relying either on sampling-based strategies or on deterministic approximations that, however, still experience computational and accuracy issues, especially in high dimensions. The scope of this article is to review, unify and extend recent advances in Bayesian inference and computation for this core class of models. To address such a goal, we prove that the likelihoods induced by these formulations share a common analytical structure implying conjugacy with a broad class of distributions, namely the unified skew-normal (SUN), that generalize Gaussians to include skewness. This result unifies and extends recent conjugacy properties for specific models within the class analyzed, and opens new avenues for improved posterior inference, under a broader class of formulations and priors, via novel closed-form expressions, i.i.d. samplers from the exact SUN posteriors, and more accurate and scalable approximations from variational Bayes and expectation-propagation. Such advantages are illustrated in simulations and are expected to facilitate the routine-use of these core Bayesian models, while providing novel frameworks for studying theoretical properties and developing future extensions.</p
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
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
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