1,720,954 research outputs found
A Neural Network approach to measure health insurance risk
Full text linkThis work presents a set of neural network applications to health insurance pricing. In recent years, the actuarial literature involving machine learning in insurance
pricing has flourished. However, most actuarial machine learning research focuses on car and property and casualty insurance. While, the use of such techniques in health insurance is yet to be explored. In this manuscript, we discuss the use of neural networks to set the price of an health insurance coverage following the structure of a classical frequency-severity model. We consider neural networks to estimate claim frequency and severity. In particular, we introduce Negative Multinomial Neural
Networks to jointly model the frequency of possibly correlated medical claims. We then complete the frequency-severity approach proposing Gamma Neural Networks
to estimate the expected claim severity. We then go beyond the frequency-severity framework adopting a quantile approach that allows gauging the potential riskiness of a given policyholder. Namely, we discuss the estimation of conditional quantiles of aggregate claim amounts embedding the problem in a quantile regression framework using the Neural Network approach. As the first step, we consider Quantile Regression Neural Networks (QRNN) to compute quantiles for the insurance ratemaking framework. As the second step, we propose a new Quantile Regression Combined Actuarial Neural Network (Quantile-CANN) combining the traditional quantile regression approach with a Quantile Regression Neural Network. In both cases, we adopt a two-part model scheme where we fit a logistic regression to estimate the probability of positive claims and the QRNN model or the Quantile-CANN for the positive outcomes. Through a case study based on a health insurance dataset, we highlight the overall better performances of the different neural network models with respect to more established regression models (such as GLMs and quantile regression), both in terms of accuracy and risk diversification
Cross‐Country assessment of systemic risk in the European Stock Market: evidence from a CoVaR analysis
No full textThis work is intended to assess the contribution to systemic risk of major companies
in the European stock market on a geographical basis. We use the EuroStoxx 50
Index as a proxy for the financial system and we rely on the CoVaR and Delta-CoVaR risk
measures to estimate the contribution of each European country belonging to the index to
systemic risk. We also conduct the significance and dominance test to evaluate whether
the systemic relevance of considered countries is statistically significant and to determine
which nation exerts the greatest influence on the spreading of negative spillover effects on
the entire economy. Our empirical results show that, for the period ranging from 2008 to
2017, all countries contribute significantly to systemic risk, especially in times of crisis and
high volatility in the markets. Moreover, it emerges that France is the systemically riskiest
country, followed by Germany, Italy, Spain and Netherlands
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
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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