7 research outputs found

    Health insurance pricing with generalised linear models

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    Mestrado em Actuarial ScienceOs Modelos Lineares Generalizados (GLMs) são amplamente utilizados na precificação de seguros do ramo Não Vida. O prémio cobrado pela seguradora é calculado com base em uma tarifa. A abordagem clássica para estimar o prémio é feita assumindo a independência entre o número de sinistros e o seu custo. A partir desta independência, a frequência e a severidade dos sinistros são estimados através de GLMs separados e a tarifa é obtida combinando os dois modelos. O presente relatório fornece uma breve introdução sobre a metodologia e descreve como preparámos os dados antes da aplicação do GLM. Os modelos obtidos para os Tratamentos e Consultas de Estomatologia, uma das muitas coberturas que podem ser incluídas numa apólice de Seguro Saúde, são analisados neste relatório. O software SAS foi utilizado para construir as bases de dados e para organizar adequadamente a informação e o software R foi utilizado para o processo de modelagem. Uma vez estimados os modelos, o prémio puro foi calculado e a tarifa, para a cobertura mencionada, foi construída. Por fim, comparámos os resultados obtidos em R com as conclusões obtidas pelos meus colegas, utilizando o software implementado pela empresa. Concluímos que ambos os modelos não são significativamente diferentes, apesar de apresentarem algumas distinções estruturais.Generalized Linear Models (GLMs) are being broadly used in the Non-Life Insurance Pricing. The premium charged by the insurance company is calculated based on a tariff. The most standard procedure to estimate the pure premium is by assuming that the claim counts and claim amounts are independent. From this independence, the claim frequency and severity can be forecasted by distinct GLMs and the Tariff is obtained by combining both models. The present report gives a brief introduction on the methodology and describes how we prepared the data prior to the GLM application. The models obtained for the Stomatology Treatments and Appointments, one of the many coverages that can be included in a Health Insurance policy, are analyzed in this report. The SAS software was used to construct the datasets and to properly organize the data and R was the software used for the modelling process. Once the models were estimated, the pure premium was calculated and a tariff for the mentioned coverage was constructed. Finally, we compared the results obtained by modelling the coverage in R with the output obtained by my colleagues, using the software implemented by the company. We conclude that both models are not significantly different, despite having some structural distinctions.info:eu-repo/semantics/publishedVersio

    Pervasive gaps in Amazonian ecological research

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    Pervasive gaps in Amazonian ecological research

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    Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

    Pervasive gaps in Amazonian ecological research

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    Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

    Pervasive gaps in Amazonian ecological research

    Get PDF
    Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

    Characterisation of microbial attack on archaeological bone