1,720,984 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
Asymptotic behavior of Bayes estimators for hidden Markov models with application to ion channels
No full textIn this paper we study the asymptotic behavior of Bayes estimators for hidden Markov models as the number of observations goes to infinity. The theorem that we prove is similar to the Bernstein-von Mises theorem on the asymptotic behavior of the posterior distribution for the case of independent observations. We show that our theorem is applicable to a wide class of hidden Markov models. We also discuss the implication of the theorem's assumptions for several models that are used in practical applications such as ion channel kinetics. © 2008 Allerton Press, Inc
Model selection and parameter estimation for ion channel recordings with an application to the K+ outward-rectifier in barley leaf
No full textWe present a statistical method, and its accompanying algorithms, for the selection of a mathematical model of the gating mechanism of an ion channel and for the estimation of the parameters of this model. The method assumes a hidden Markov model that incorporates filtering, colored noise and state-dependent white excess noise for the recorded data. The model selection and parameter estimation are performed via a Bayesian approach using Markov chain Monte Carlo. The method is illustrated by its application to single-channel recordings of the
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
Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo
No full textIon channels are proteins that are located in the membranes of cells and are capable of conducting ions through the membrane. An ion channel is not always 'open' for transport. The ion channel molecule may reside in several configurations, some of which correspond to an open channel and others to a closed channel. The transitions of the channel between the different configurational states have a random nature. Markov processes are often used to describe this randomness. In practice, there often exist a number of candidate Markov models. The objective of this paper is the selection of a Markov model from a finite collection of such models. We propose a Bayesian setting in which the model indicator itself is viewed as a random variable, and we develop a reversible jump Markov chain Monte Carlo (MCMC) algorithm in order to generate a sample from the posterior distribution of the model indicator given the data of a single-channel recording. A hidden Markov model is used to incorporate the correlated noise in recordings and the effects of filters that are present in the experimental set-up. The reversible jump MCMC sampler is applied to both simulated and recorded data sets. © 2003 ISI/BS
Data integrative Bayesian inference for mixtures of regression models
Get PDFModern data collection techniques, which often produce different types of relevant information, call for new statistical learning methods that are adapted to cope with data integration. In the paper Bayesian inference is considered for mixtures of regression models with an unknown number of components, that facilitates data integration and variable selection for high dimensional data. In the approach presented, named data integrative mixture of regressions, data integration is accomplished by introducing a new data allocation scheme that summarizes additional data in the form of an informative prior on latent variables. To cope with high dimensionality, a shrinkage-type prior is assumed on the regression parameters, and a posteriori variable selection is conducted based on Bayesian credible intervals. Posterior estimation is achieved via a Markov chain Monte Carlo algorithm. The method is validated through simulation studies and illustrated by its performance on real data
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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