LEADER 02709nam 2200637 450 001 9910138995703321 005 20200520144314.0 010 $a1-118-65064-6 010 $a1-118-65055-7 010 $a1-118-65057-3 035 $a(CKB)2550000001134409 035 $a(EBL)1481181 035 $a(OCoLC)845350281 035 $a(SSID)ssj0001039363 035 $a(PQKBManifestationID)11577121 035 $a(PQKBTitleCode)TC0001039363 035 $a(PQKBWorkID)10985078 035 $a(PQKB)11345950 035 $a(OCoLC)866835864 035 $a(MiAaPQ)EBC1481181 035 $a(DLC) 2013021633 035 $a(Au-PeEL)EBL1481181 035 $a(CaPaEBR)ebr10783654 035 $a(CaONFJC)MIL534117 035 $a(EXLCZ)992550000001134409 100 $a20130529d2013 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe law of fundraising /$fBruce R. Hopkins, Alicia Kirkpatrick 205 $aFifth Edition. 210 1$aHoboken, New Jersey :$cWiley,$d2013. 215 $a1 online resource (638 p.) 225 1 $aWiley Nonprofit Authority 300 $aIncludes index. 311 $a1-118-65063-8 311 $a1-306-02866-3 327 $aPreface -- Acknowledgments -- About the authors -- About the companion website -- Government regulation of fundraising for charity -- Anatomy of charitable fundraising -- States' charitable solicitation acts -- State regulation of fundraising : legal issues -- Index. 330 $aRaising funds to fulfill a nonprofit organization's goals is critical to its success, but fundraising regulations are an increasingly complex maze. The Law of Fundraising, Fifth Edition is the definitive guide to demystifying federal and state fundraising regulations. With new discussion on Internet fundraising, political fundraising laws, and international fundraising, this book details federal and state laws, with an emphasis on administrative, tax, and constitutional laws. This guide is supplemented annually to keep nonprofit professionals on top of the latest fundraising legal 410 0$aWiley Nonprofit Authority 606 $aCharitable uses, trusts, and foundations$zUnited States 606 $aFund raising$xLaw and legislation$zUnited States 615 0$aCharitable uses, trusts, and foundations 615 0$aFund raising$xLaw and legislation 676 $a346.73/064 700 $aHopkins$b Bruce R$0279523 701 $aKirkpatrick$b Alicia$f1984-$0981486 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910138995703321 996 $aThe law of fundraising$92240162 997 $aUNINA LEADER 03881nam 2200949z- 450 001 9910557660803321 005 20210501 035 $a(CKB)5400000000044898 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68741 035 $a(oapen)doab68741 035 $a(EXLCZ)995400000000044898 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning in Insurance 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (260 p.) 311 08$a3-03936-447-2 311 08$a3-03936-448-0 330 $aMachine 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. 606 $aHistory of engineering and technology$2bicssc 610 $aaccelerated failure time model 610 $aanalyzing financial data 610 $aautocorrelation 610 $aautomobile insurance 610 $abenchmark 610 $aBornhuetter-Ferguson 610 $acalibration 610 $acanonical parameters 610 $achain ladder 610 $achain-ladder method 610 $aclaims prediction 610 $across-validation 610 $adeposit insurance 610 $adichotomous response 610 $aexponential families 610 $aexport credit insurance 610 $ageneralised linear modelling 610 $aGLM 610 $aimplied volatility 610 $aleast-squares monte carlo method 610 $alife insurance 610 $alocal linear kernel estimation 610 $along-term forecasts 610 $amachine learning 610 $amaximum likelihood 610 $an/a 610 $anon-life reserving 610 $aoperational time 610 $aoverdispersion 610 $aoverlapping returns 610 $aparameterization 610 $aprediction 610 $apredictive model 610 $aprior knowledge 610 $aproxy modeling 610 $arisk classification 610 $arisk selection 610 $asemiparametric modeling 610 $aSolvency II 610 $astatic arbitrage 610 $astock return volatility 610 $atelematics 610 $atree boosting 610 $avalidation 610 $aVaR estimation 610 $azero-inflated poisson model 610 $azero-inflation 615 7$aHistory of engineering and technology 700 $aNielsen$b Jens Perch$4edt$01314788 702 $aAsimit$b Alexandru$4edt 702 $aKyriakou$b Ioannis$4edt 702 $aNielsen$b Jens Perch$4oth 702 $aAsimit$b Alexandru$4oth 702 $aKyriakou$b Ioannis$4oth 906 $aBOOK 912 $a9910557660803321 996 $aMachine Learning in Insurance$93031967 997 $aUNINA