LEADER 02179nam 2200445 n 450 001 996392502103316 005 20200824121738.0 035 $a(CKB)4940000000114286 035 $a(EEBO)2264196124 035 $a(UnM)ocm99892554e 035 $a(UnM)99892554 035 $a(EXLCZ)994940000000114286 100 $a19960507d1551 uy 101 0 $aeng 135 $aurbn||||a|bb| 200 12$aA proclamacion, set furthe by the kynges Maiestie, with the aduise of his highnes moste honorable counsaill, for the publishyng of sundery statutes and actes of Parliament heretofore made, for the prohibicion of the cariyng out of the realme of gold or silver, and of eschaunge and reeschaunge$b[electronic resource] 210 $a[London] $cRichardus Grafton typographus regius excudebat$dmense Iunij. Anno. 1551 215 $a1 sheet ([1] p.) 300 $aInitial. 300 $a"No man to exchange gold or silver, coin, bullion, or vessels without a licence from the King. Laws (3 Hen. 7) against exchange and export of precious metal to be strictly enforced." -- Steele. 300 $aPlace of publication from STC. 300 $aAfter imprint: Cum priuilegio ad imprimendum solum. 300 $aSteele notation: displeasure gyng pro-. 300 $aReproduction of original in the British Library. 330 $aeebo-0018 606 $aGold$zEngland$vEarly works to 1800 606 $aSilver$zEngland$vEarly works to 1800 606 $aPrecious metals$xLaw and legislation$zEngland$vEarly works to 1800 606 $aMoney$vEarly works to 1800 615 0$aGold 615 0$aSilver 615 0$aPrecious metals$xLaw and legislation 615 0$aMoney 701 $aEdward$cKing of England,$f1537-1553.$01002269 801 0$bCu-RivES 801 1$bCu-RivES 906 $aBOOK 912 $a996392502103316 996 $aA proclamacion, set furthe by the kynges Maiestie, with the aduise of his highnes moste honorable counsaill, for the publishyng of sundery statutes and actes of Parliament heretofore made, for the prohibicion of the cariyng out of the realme of gold or silver, and of eschaunge and reeschaunge$92371147 997 $aUNISA LEADER 03013nam 2200505z- 450 001 9910404090203321 005 20210211 010 $a3-03928-665-X 035 $a(CKB)4100000011302236 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/43322 035 $a(oapen)doab43322 035 $a(EXLCZ)994100000011302236 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aClaim Models: Granular Forms and Machine Learning Forms 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (108 p.) 311 08$a3-03928-664-1 330 $aThis 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. 517 $aClaim Models 606 $aPharmaceutical chemistry and technology$2bicssc 610 $aactuarial 610 $aclaim watching 610 $aclassification and regression trees 610 $agradient boosting 610 $agranular models 610 $aindividual claims reserving 610 $aindividual models 610 $aloss reserving 610 $amachine learning 610 $an/a 610 $aneural networks 610 $apayments per claim incurred 610 $apredictive modeling 610 $arisk pricing 615 7$aPharmaceutical chemistry and technology 700 $aTaylor$b Greg$4auth$0617436 906 $aBOOK 912 $a9910404090203321 996 $aClaim Models: Granular Forms and Machine Learning Forms$93040130 997 $aUNINA