1,720,992 research outputs found
Mixed-effects modelling of Kevlar fibre failure times throughBayesian nonparametrics.
No full textWe examine the accelerated failure time model for
univariate failure time data with right censoring, with application
to the failure times of Kevlar fibres from different spools,
subject to different stress levels.
We propose a semi-parametric modelling by letting the error
distribution be a shape-scale mixture of Weibull densities, the mixing
measure being a normalized
generalized gamma measure. We obtain posterior estimates of the
regression parameter and also of credibility intervals for
the predictive distributions and their quantiles, by including in the
MCMC scheme the posterior distribution of the random mixing
probability.
The number of components in the nonparametric mixture can be
interpreted as the number of random effects, having a prior distribution
induced by the nonparametric model, and is inferred from the data.
Compared to previous results, we obtain narrower interval estimates of
the quantiles of the predictive survival function.
Other diagnostic plots, such as predictive tails and Bayesian residuals, show a good agreement between the model and the data
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
Joint Prediction of Demand and Care Duration in Home Care Patients: a Bayesian Approach Previsione Congiunta della Domanda e della Durata di Cura in Assistenza Domiciliare: un Approccio Bayesiano
No full textHome Care (HC) service is an alternative to conventional hospitalization and consists of delivering medical, paramedical and social services to patients at their homes rather than in hospitals or nursing homes. Human resource planning in HC is a difficult task and, for a good quality of planning, knowledge of future patients’ demands is required. The aim of this paper is to propose a Bayesian model for predicting the number of visits required by HC patients, which is fundamental for planning human and material resources, and at the same time describing the natural history of Care Profiles. We model patients’ holding times, i.e., the duration of Care Profiles, and the number of nurses’ visits at each future time slot. The model has been applied to the real data of one of the largest public HC providers in Italy. We computed the estimates of all model parameters and the predictions for both new patients and patients already in the charge. Preliminary results show the applicability of the approach in the practice and good quality of predictions
Bayesian principal curve clustering by species-sampling mixture models Clustering mediante modelli mistura a campionamento di specie di curve principali bayesiane
No full textIn this work we are interested in clustering data whose support is “curved”. For this purpose, we will follow a Bayesian nonparametric approach by consider- ing a species sampling mixture model. Our first goal is to define a general/flexible class of distributions, such that they can model data from clusters with non standard shape. To this end, we extend the definition of principal curve given in [8] (Tibshi- rani 1992) into a Bayesian framework. We propose a new hierarchical model, where the data in each cluster are parametrically distributed around the Bayesian principal curve, and the prior cluster assignment is given on the latent variables at the second level of hierarchy according to a species sampling model. As an application we will consider the detection of seismic faults using data coming from Italian earthquake catalogues
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
Bias Correction in Clustered Underreported Data
Full text linkData quality from poor and socially deprived regions have given rise
to many statistical challenges. One of them is the underreporting of vital events
leading to biased estimates for the associated risks. To deal with underreported
count data, models based on compound Poisson distributions have been commonly
assumed. To be identifiable, such models usually require extra and strong information
about the probability of reporting the event in all areas of interest, which
is not always available. We introduce a novel approach for the compound Poisson
model assuming that the areas are clustered according to their data quality. We
leverage these clusters to create a hierarchical structure in which the reporting
probabilities decrease as we move from the best group to the worst ones.We obtain
constraints for model identifiability and prove that only prior information about
the reporting probability in areas experiencing the best data quality is required.
Several approaches to model the uncertainty about the reporting probabilities are
presented, including reference priors. Different features regarding the proposed
methodology are studied through simulation. We apply our model to map the
early neonatal mortality risks in Minas Gerais, a Brazilian state that presents
heterogeneous characteristics and a relevant socio-economical inequality
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