01333nam--2200397---450-99000163272020331620060131103644.0000163272USA01000163272(ALEPH)000163272USA0100016327220040504d19..----km-y0itay0103----baengITa|||||||001yyCatalogue of western European paintingthe HermitageNew YorkJohnson reprint-Harcourt Brace JovanovichFirenzeGiunti-Martello19..-v.ill.34 cm<11.> : French painting : early and mid-nineteenth century / by Valentina N. Berenzina. - 1983. - 551 P.20012001001-------2001<<11.>> : French painting : early and mid-nineteenth centuryPittureLeningradoMuseo dell'Ermitage759BERENZINA,Valentina NikolaevnaGosudarstvennyj Ermitaz179076ITsalbcISBD990001632720203316XII.2.C. 856/11(VII P 37/11)17777 L.M.VII PBKUMASIAV51020040504USA011419COPAT69020060131USA011036Catalogue of western European painting943585UNISA03013nam 2200505z- 450 9910404090203321202102113-03928-665-X(CKB)4100000011302236(oapen)https://directory.doabooks.org/handle/20.500.12854/43322(oapen)doab43322(EXLCZ)99410000001130223620202102d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierClaim Models: Granular Forms and Machine Learning FormsMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (108 p.)3-03928-664-1 This collection of articles addresses the most modern forms of loss reserving methodology: granular models and machine learning models. New methodologies come with questions about their applicability. These questions are discussed in one article, which focuses on the relative merits of granular and machine learning models. Others illustrate applications with real-world data. The examples include neural networks, which, though well known in some disciplines, have previously been limited in the actuarial literature. This volume expands on that literature, with specific attention to their application to loss reserving. For example, one of the articles introduces the application of neural networks of the gated recurrent unit form to the actuarial literature, whereas another uses a penalized neural network. Neural networks are not the only form of machine learning, and two other papers outline applications of gradient boosting and regression trees respectively. Both articles construct loss reserves at the individual claim level so that these models resemble granular models. One of these articles provides a practical application of the model to claim watching, the action of monitoring claim development and anticipating major features. Such watching can be used as an early warning system or for other administrative purposes. Overall, this volume is an extremely useful addition to the libraries of those working at the loss reserving frontier.Claim ModelsPharmaceutical chemistry and technologybicsscactuarialclaim watchingclassification and regression treesgradient boostinggranular modelsindividual claims reservingindividual modelsloss reservingmachine learningn/aneural networkspayments per claim incurredpredictive modelingrisk pricingPharmaceutical chemistry and technologyTaylor Gregauth617436BOOK9910404090203321Claim Models: Granular Forms and Machine Learning Forms3040130UNINA