1,721,061 research outputs found
Sparse logistic maximum likelihood estimation for optimal well-being determinants
No full textOptimal well-being is a new multi-dimensional construct, which incorporates the well-known preexisting notions of subjective and psychological well-being. Classical models for describing the predictors of optimal well-being binary response variable are generalized linear models (GLM), such as logistic regression model. Since the number of predictors might be relatively large in these models, we devise a sparse optimization method for the regression problem based on subsequent iterations of a suitable sparse quadratic approximant problem, so that the resulting parameter vector estimate is sparse and indicates few significant predictors. We conduct empirical assessments using data of the European Social Survey, in order to identify the set of determinants which better predict optimal well-being by means of the proposed sparse regression method. ESS data analysis confirms that few selected predictors provide good data interpretation and no loss of information in the frequency of correct classification for people meeting the criteria of optimal well-being. Moreover, simulations with different structural parameter values indicate that sparse logistic model performs better in terms of the estimation of the true vector of parameters in a more parsimonious setting compared to classical logistic regression. The benefits increase as the structural sparsity of the optimization problem becomes stronger
Local limit laws for symbol statistics in bicomponent rational models
No full textWe study the local limit distribution of the number of occurrences of a symbol in words of length n generated at random in a regular language according to a rational stochastic model. We present an analysis of the main local limits when the finite state automaton defining the stochastic model consists of two primitive components. The limit distributions depend on several parameters and conditions, such as the main constants of mean value and variance of our statistics associated with the two components, and the existence of communications from the first to the second component. The convergence rate of these results is always of order . For the same statistics we also prove an analogous convergence rate of the Gaussian local limit law whenever the stochastic model consists of one primitive component
Supervised Ensemble-based Causal DAG Selection
No full textCausal Discovery (CD) identifes cause-and-effect relationships from data using statistical learning. Several CD algorithms have been proposed relying on different assumptions, e.g. about the statistical relations among variables. However, which assumptions actually hold for a specifc case study is not known a priori. Given a dataset obtained by sampling the joint distribution of all variables of a generative causal model, in general each algorithm could reconstruct a different Direct Acyclic Graph (DAG): some will be closer to the ground truth (GT) DAG than others, depending also on the applicability of the respective assumptions to the case study. As a consequence, given a collection of heterogeneous case studies, a hypothetical GT-aware oracle, able to select the best DAG out of the set of reconstructed DAGs, will outclass the average performance of the individual algorithms of the ensemble. In this work, we propose a supervised approach, relying on multilabel classifcation, to select the DAGs closest to GT by only comparing the topologies of the reconstructed DAGs. We carried out the study on a wide synthetic data set of causal models, sampling DAG topologies up to ten vertices, and using a representative set of linear and non-linear statistical dependencies. Whereas the best individual CD algorithm yields, on average, a distance from GT three times larger than the oracle, our algorithm features an average distance from GT only about 10% larger than the oracle
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
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
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
A Vertex-centric Markov Chain Algorithm for Network Clustering based on b-Coloring
No full textThe massive size and complexity of big datasets such as those coming from social, natural and sensor environments raise utmost challenges to unsupervised cluster analysis methods in terms of performance scalability in designing algorithms, also considering parallel and distributed networking context. To cope with these hindrances, the parallelization of clustering techniques, also benefiting from GPU-centered computation, can contribute to fill the gap in applicative areas such as optimization of network routing or management of large-scale IoT networks, thus enabling the extraction, processing and policy making relying on rich network information that are typically represented in the form of graphs. One established approach to clustering graphs is through the coloring techniques, and indeed, graph clustering and graph coloring can be viewed as tied. We devise a graph clustering technique based on a Markov Chain method aimed at b-coloring the data points, that works in efficient vertex-centric parallel manner and produces a valid clustering with reduced number of color classes. We assess our algorithm against synthetic data encapsulating group structure characteristics and present a brief convergence analysis of the method
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