1,721,004 research outputs found
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
Uniform random generation of large acyclic digraphs
No full textDirected acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applications, such as the reverse engineering of gene regulatory networks, not only the estimation of model parameters but the reconstruction of the structure itself is of great interest. As well as for the assessment of different structure learning algorithms in simulation studies, a uniform sample from the space of directed acyclic graphs is required to evaluate the prevalence of certain structural features. Here we analyse how to sample acyclic digraphs uniformly at random through recursive enumeration, an approach previously thought too computationally involved. Based on complexity considerations, we discuss in particular how the enumeration directly provides an exact method, which avoids the convergence issues of the alternative Markov chain methods and is actually computationally much faster. The limiting behaviour of the distribution of acyclic digraphs then allows us to sample arbitrarily large graphs. Building on the ideas of recursive enumeration based sampling we also introduce a novel hybrid Markov chain with much faster convergence than current alternatives while still being easy to adapt to various restrictions. Finally we discuss how to include such restrictions in the combinatorial enumeration and the new hybrid Markov chain method for efficient uniform sampling of the corresponding graphs
The Variance of Causal Effect Estimators for Binary V-structures
No full textAdjusting for covariates is a well established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study there may be different adjustment sets, equally valid from a theoretical perspective, leading to identical causal effects. However, in practice, with finite data, estimators built on different sets may display different precision. To investigate the extent of this variability we consider the simplest non-trivial non-linear model of a v-structure on three nodes for binary data. We explicitly compute and compare the variance of the two possible different causal estimators. Further, by going beyond leading order asymptotics we show that there are parameter regimes where the set with the asymptotically optimal variance does depend on the edge coefficients, a result which is not captured by the recent leading order developments for general causal models. As a practical consequence, the adjustment set selection needs to account for the relative magnitude of the relationships between variables with respect to the sample size, and cannot rely on purely graphical criteria
Incidence and characteristics of adverse events in paediatric inpatient care : a systematic review and meta-analysis
Full text linkBACKGROUND: Adverse events (AEs) cause suffering for hospitalised children, a fragile patient group where the delivery of adequate timely care is of great importance. OBJECTIVE: To report the incidence and characteristics of AEs, in paediatric inpatient care, as detected with the Global Trigger Tool (GTT), the Trigger Tool (TT) or the Harvard Medical Practice Study (HMPS) method. METHOD: MEDLINE, Embase, Web of Science and Google Scholar were searched from inception to June 2021, without language restrictions. Studies using manual record review were included if paediatric data were reported separately. We excluded studies reporting: AEs for a specific disease/diagnosis/treatment/procedure, or deceased patients; study protocols with no AE outcomes; conference abstracts, editorials and systematic reviews; clinical incident reports as the primary data source; and studies focusing on specific AEs only. Methodological risk of bias was assessed using a tool based on the Quality Assessment Tool for Diagnostic Accuracy Studies 2. Primary outcome was the percentage of admissions with ≥1 AEs. All statistical analyses were stratified by record review methodology (GTT/TT or HMPS) and by type of population. Meta-analyses, applying random-effects models, were carried out. The variability of the pooled estimates was characterised by 95% prediction intervals (PIs). RESULTS: We included 32 studies from 44 publications, conducted in 15 countries totalling 33 873 paediatric admissions. The total number of AEs identified was 8577. The most common types of AEs were nosocomial infections (range, 6.8%-59.6%) for the general care population and pulmonary-related (10.5%-36.7%) for intensive care. The reported incidence rates were highly heterogeneous. The PIs for the primary outcome were 3.8%-53.8% and 6.9%-91.6% for GTT/TT studies (general and intensive care population). The equivalent PI was 0.3%-33.7% for HMPS studies (general care). The PIs for preventable AEs were 7.4%-96.2% and 4.5%-98.9% for GTT/TT studies (general and intensive care population) and 10.4%-91.8% for HMPS studies (general care). The quality assessment indicated several methodological concerns regarding the included studies. CONCLUSION: The reported incidence of AEs is highly variable in paediatric inpatient care research, and it is not possible to estimate a reliable single rate. Poor reporting standards and methodological differences hinder the comparison of study results.Funding This study was funded by grants from a regional agreement on clinical research (ALF) between Region Stockholm and Karolinska Institutet (2020- 0443), Childhood Foundation of the Swedish Order of Freemasons (no award/ grant number).</p
Bayesian approaches to discovery and inference with non-linear causal models
Full text linkCausal discovery and inference from observational data is an essential problem in statistics posing both modeling and computational challenges. Bayesian network models provide a rigorous approach to the problem by compactly describing the joint distribution and the causal relationships through a graph. When the underlying graph is unknown, structure learning methods are used to estimate it from the available data. Such a task poses considerable challenges, but is key to describing data-generating mechanisms in many complex applications. This thesis presents new developments in structure learning and causal inference, with a focus on computational efficiency and non-parametric, Bayesian approaches for observational data.
Firstly, we introduce a new constraint-based structure learning algorithm based on the popular PC algorithm. The new method, called the dual PC algorithm, leverages the inverse relationship between covariance and precision matrices. By exploiting block matrix inversions it can also perform tests on partial correlations of complementary (or dual) conditioning sets. The dual PC algorithm proceeds by first considering marginal and full-order conditional independence relationships and progressively moving to central-order ones. Simulation studies show that the dual PC algorithm outperforms the classic PC algorithm both in terms of run time and in recovering the underlying network structure. We also study the effects of deviations from Gaussianity on the performance of the classical and dual PC algorithms, and propose alternative approaches when consistency no longer holds.
Next, we address the problem of learning the structure of Gaussian Process Networks (GPNs), a class of Bayesian networks which employ Gaussian processes as priors for the conditional expectation of each variable given its parents. We adopt a Bayesian approach, accounting for uncertainty in the graphical estimate via a posterior distribution over structures. We show how Monte Carlo and Markov chain Monte Carlo methods can be used to sample from the graph posterior distribution, allowing accurate posterior inference in high-dimensional cases. Our method outperforms state-of-the-art algorithms in recovering the graphical structure of the network in various non-linear simulation settings. The proposed method is also shown to provide an accurate approximation of the network's posterior distribution.
Finally, we consider the problem of the Bayesian estimation of the effects of hypothetical interventions in the GPN model. We detail how to perform causal inference on GPNs by simulating the effect of an intervention across the whole network and propagating the effect of the intervention on downstream variables. A simpler computational approximation can be derived by estimating the intervention distribution as a function of local variables only, modeling the conditional distributions via additive Gaussian processes. We extend both frameworks beyond the case of a known causal graph, incorporating uncertainty about the causal structure using the previously developed Bayesian structure learning methods. Simulation studies show that our approach is able to identify the effects of hypothetical interventions with non-Gaussian, non-linear observational data and accurately reflect the posterior uncertainty of the causal estimates. Finally we compare the results of our GPN-based causal inference approach to existing methods on a dataset of A. thaliana gene expressions
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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