1,721,027 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
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Statistical Inference in Neuroimaging Analysis
No full textThe dissertation makes contributions to two critical inferential problems in brain science. The first problem is detecting effective connectivity from time series measurements of brain activity. Chapter 2 studies effective connectivity inference for a specific subject. We develop both global and simultaneous testing procedures to connectivity pattern, and establish their asymptotic guarantees. We show the finite-sample performance of tests through intensive simulations, and illustrate with a neuroimaging based brain connectivity analysis. Chapter 3 extends the scenario to multi-subject effective connectivity inference. The motivation originates from elucidating subject covariate effects on brain effective connectivity in multi-subject function resonance imaging experiments. We propose a new model to explain how covariates change connectivity patterns among subjects. We develop a testing procedure on the model parameters of covariate effects with false discovery rate (FDR) control. Thorough numerical experiments and a HCP fMRI data analysis demonstrate the superior performance of the method. The second problem is recovering strong association between brain cognitive function and regional cortical physiological features. Such an association is complex and nonlinear that existing solutions in linear models are inadequate to capture. Chapter 4 studies detecting the strong association, equivalently variable selection, in nonparametric additive model. We show that the proposed method is guaranteed to control FDR even the sample size does not tend to infinity, and achieves a power that approaches one as the sample size tends to infinity. We demonstrate the efficacy of the method through intensive simulations and comparisons with the alternative solutions
Variations on the Author
Get PDF“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
Get PDFWe 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
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Advancing Statistical Methods and Applications for Complex Systems
Get PDFThis dissertation develops novel statistical methodologies and computational approaches to address key challenges in mediation analysis, multimodal data integration, and equitable resource allocation in healthcare. It comprises four thematically connected studies, with the first two closely related and the latter two addressing distinct challenges.The first two studies focus on sensitivity analysis in causal mediation modeling. The first introduces a sensitivity analysis method for the Baron-Kenny approach to mediation, which estimates direct and indirect effects using linear models. When unmeasured confounding exists, these estimates may be biased. To address this issue, this study derives general omitted-variable bias formulas for linear regressions with vector responses and regressors and applies them to develop an interpretable sensitivity analysis framework. It also introduces the “robustness value for mediation”, a novel measure that quantifies the minimum level of unmeasured confounding required to overturn mediation results. The second study extends this work to path-specific effects in linear structural equation models, developing sensitivity analysis techniques that assess the robustness of mediation pathways under unmeasured confounding.The third study develops a multivariate conditional correlation regression framework to address a key challenge in multimodal integrative analysis—understanding how two data modalities associate and interact given a third modality. This study models the three-way association (X, Y) | Z, where X, Y, and Z are random vectors representing different modalities of interest. By extending the conditional bivariate normal model to multivariate settings, the framework identifies sparse linear combinations of X, Y, and Z that maximize the contrast in correlation between X and Y as Z varies. Post dimension reduction theory establishes theoretical guarantees for parameter estimation.The final study introduces a fairness-aware optimization framework for kidney paired donation (KPD), which facilitates kidney exchanges among incompatible donor-patient pairs. To address disparities in transplant access, this study proposes a new fairness criterion based on the calibration principle in machine learning: the matching outcome should be conditionally independent of a protected feature (e.g., race, gender), given the sensitization level. The study integrates this fairness criterion as a constraint within the KPD optimization framework and develops a computationally efficient solution.These studies contribute to the broader theme of “Advancing Statistical Methods and Applications for Complex Systems.” The proposed methodologies strengthen both theoretical foundations and practical applications in causal inference, multimodal neuroimaging, and fairness-aware decision-making
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
UFRGS Mobilizada
No full textA chinesa Li Lexin, no Brasil chamada de Tatiana, estava na UFRGS estudando Português para Estrangeiros, e teve que adiantar seu retorno para casa, em função da pandemia do novo coronavírus.Vídeo cedido pela font
UFRGS Mobilizada
No full textA chinesa Li Lexin, no Brasil chamada de Tatiana, estava na UFRGS estudando Português para Estrangeiros, e teve que adiantar seu retorno para casa, em função da pandemia do novo coronavírus.Vídeo cedido pela font
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