01981oam 2200589 450 991071662060332120210830110205.0(CKB)5470000002523963(OCoLC)761279680(OCoLC)995470000002523963(EXLCZ)99547000000252396320111115j198604 ua 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierLow-speed wind-tunnel tests of single- and counter-rotation propellers /Dana Morris Dunham, Garl L. Gentry, Jr., and Paul L. Coe, JrWashington, DC :National Aeronautics and Space Administration, Scientific and Technical Information Branch,April 1986.1 online resource (43 pages, 1 unnumbered page) illustrationsNASA/TM ;87656"April 1986."Includes bibliographical references (page 20).AerodynamicsnasatTurboprop aircraftnasatPropellersnasatPropellers, AerialTestingPropellers, AerialTestingfastAerodynamics.Turboprop aircraft.Propellers.Propellers, AerialTesting.Propellers, AerialTesting.Dunham Dana Morris1412851Gentry Garl L.Coe Paul L.United States.National Aeronautics and Space Administration.Scientific and Technical Information Branch,Langley Research Center.OCLCEOCLCEOCLCQOCLCFOCLCOOCLCQGPOOCLCOOCLCQGPOBOOK9910716620603321Low-speed wind-tunnel tests of single- and counter-rotation propellers3507725UNINA03881nam 2200949z- 450 991055766080332120210501(CKB)5400000000044898(oapen)https://directory.doabooks.org/handle/20.500.12854/68741(oapen)doab68741(EXLCZ)99540000000004489820202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning in InsuranceBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (260 p.)3-03936-447-2 3-03936-448-0 Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries' "preferred methods" were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.History of engineering and technologybicsscaccelerated failure time modelanalyzing financial dataautocorrelationautomobile insurancebenchmarkBornhuetter-Fergusoncalibrationcanonical parameterschain ladderchain-ladder methodclaims predictioncross-validationdeposit insurancedichotomous responseexponential familiesexport credit insurancegeneralised linear modellingGLMimplied volatilityleast-squares monte carlo methodlife insurancelocal linear kernel estimationlong-term forecastsmachine learningmaximum likelihoodn/anon-life reservingoperational timeoverdispersionoverlapping returnsparameterizationpredictionpredictive modelprior knowledgeproxy modelingrisk classificationrisk selectionsemiparametric modelingSolvency IIstatic arbitragestock return volatilitytelematicstree boostingvalidationVaR estimationzero-inflated poisson modelzero-inflationHistory of engineering and technologyNielsen Jens Perchedt1314788Asimit AlexandruedtKyriakou IoannisedtNielsen Jens PerchothAsimit AlexandruothKyriakou IoannisothBOOK9910557660803321Machine Learning in Insurance3031967UNINA