This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Andersson, V. O., Birck, M. A. F., & Araujo, R. M. (2017). Investigating crime rate prediction using street-level images and Siamese convolutional neural networks. In E. Teles & C. Brackmann (Eds.), Computational neuroscience (pp. 81–93). Cham, Switzerland: Springer International Publishing.AnderssonV. O.BirckM. A. F.AraujoR. M.2017Investigating crime rate prediction using street-level images and Siamese convolutional neural networksInTelesE.BrackmannC.(Eds.),8193Cham, SwitzerlandSpringer International Publishing10.1007/978-3-319-71011-2_7Search in Google Scholar
Anguelov, D., Dulong, C., Filip, D., Frueh, C., Lafon, S., Lyon, R., … Weaver, J. (2010). Google street view: Capturing the world at street level. Computer, 43(6), 32–38.AnguelovD.DulongC.FilipD.FruehC.LafonS.LyonR.WeaverJ.2010Google street view: Capturing the world at street level436323810.1109/MC.2010.170Search in Google Scholar
Bingham, C. R., Shope, J. T., & Zhu, J. (2008). Substance-involved driving: Predicting driving after using alcohol, marijuana, and other drugs. Traffic Injury Prevention, 9(6), 515–526.BinghamC. R.ShopeJ. T.ZhuJ.2008Substance-involved driving: Predicting driving after using alcohol, marijuana, and other drugs9651552610.1080/15389580802273698Search in Google Scholar
Blitz, M. J. (2012). The right to map (and avoid being mapped): Reconceiving first amendment protection for information-gathering in the age of Google Earth. The Columbia Science and Technology Law Review, 14, 115.BlitzM. J.2012The right to map (and avoid being mapped): Reconceiving first amendment protection for information-gathering in the age of Google Earth14115Search in Google Scholar
Braver, E. R. (2003). Race, Hispanic origin, and socioeconomic status in relation to motor vehicle occupant death rates and risk factors among adults. Accident; Analysis and Prevention, 35(3), 295–309.BraverE. R.2003Race, Hispanic origin, and socioeconomic status in relation to motor vehicle occupant death rates and risk factors among adults35329530910.1016/S0001-4575(01)00106-3Search in Google Scholar
Cizek, P., Härdle, W. K., & Weron, R. (2005). Statistical tools for finance and insurance. Berlin, German: Springer Science & Business Media.CizekP.HärdleW. K.WeronR.2005Berlin, GermanSpringer Science & Business MediaSearch in Google Scholar
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.EstevaA.KuprelB.NovoaR. A.KoJ.SwetterS. M.BlauH. M.ThrunS.2017Dermatologist-level classification of skin cancer with deep neural networks542763911511810.1038/nature21056Search in Google Scholar
Finer, M., Novoa, S., Weisse, M. J., Petersen, R., Mascaro, J., Souto, T., … Martinez, R. G. (2018). Combating deforestation: From satellite to intervention. Science, 360(6395), 1303–1305.FinerM.NovoaS.WeisseM. J.PetersenR.MascaroJ.SoutoT.MartinezR. G.2018Combating deforestation: From satellite to intervention36063951303130510.1126/science.aat1203Search in Google Scholar
Frees, E. W., Meyers, G., & Cummings, A. D. (2011). Summarizing insurance scores using a Gini Index. Journal of the American Statistical Association, 106(495), 1085–1098.FreesE. W.MeyersG.CummingsA. D.2011Summarizing insurance scores using a Gini Index1064951085109810.1198/jasa.2011.tm10506Search in Google Scholar
Gaulding, J. (1994). Race sex and genetic discrimination in insurance: What’s fair. Cornell Law Review, 80, 1646.GauldingJ.1994Race sex and genetic discrimination in insurance: What’s fair801646Search in Google Scholar
Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences of the United States of America, 114(50), 13108–13113.GebruT.KrauseJ.WangY.ChenD.DengJ.AidenE. L.Fei-FeiL.2017Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States11450131081311310.1073/pnas.1700035114Search in Google Scholar
Gogol, F. (1993). The Value of Information in Insurance Pricing. The Journal of Risk and Insurance, 60(1), 119–128.GogolF.1993The Value of Information in Insurance Pricing60111912810.2307/253102Search in Google Scholar
Gillis, A. R. (1974). Population density and social pathology: The case of building type, social allowance and juvenile delinquency. Social Forces; a Scientific Medium of Social Study and Interpretation, 53(2), 306–314.GillisA. R.1974Population density and social pathology: The case of building type, social allowance and juvenile delinquency53230631410.2307/2576024Search in Google Scholar
Gini, C. (1921). Measurement of inequality of incomes. The Economic Journal of Nepal, 31(121), 124–126.GiniC.1921Measurement of inequality of incomes3112112412610.2307/2223319Search in Google Scholar
Goel, R., Garcia, L. M. T., Goodman, A., Johnson, R., Aldred, R., Murugesan, M., … Woodcock, J. (2018). Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain. PloS One, 13(5), e0196521.GoelR.GarciaL. M. T.GoodmanA.JohnsonR.AldredR.MurugesanM.WoodcockJ.2018Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain135e019652110.1371/journal.pone.0196521Search in Google Scholar
Goldburd, M., Khare, A., & Tevet, C. D. (2016). Generalized linear models for insurance rating. In Casualty Actuarial Society. Retrieved from https://www.casact.org/pubs/monographs/papers/05-Goldburd-Khare-Tevet.pdf.GoldburdM.KhareA.TevetC. D.2016Generalized linear models for insurance ratingInRetrieved from https://www.casact.org/pubs/monographs/papers/05-Goldburd-Khare-Tevet.pdf.Search in Google Scholar
Golden, L. L., Brockett, P. L., Ai, J., & Kellison, B. (2016). Empirical evidence on the use of credit scoring for predicting insurance losses with psycho-social and biochemical explanations. North American Actuarial Journal: NAAJ, 20(3), 233–251.GoldenL. L.BrockettP. L.AiJ.KellisonB.2016Empirical evidence on the use of credit scoring for predicting insurance losses with psycho-social and biochemical explanations20323325110.1080/10920277.2016.1209118Search in Google Scholar
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794.JeanN.BurkeM.XieM.DavisW. M.LobellD. B.ErmonS.2016Combining satellite imagery and machine learning to predict poverty353630179079410.1126/science.aaf7894Search in Google Scholar
Karlaftis, M. G., & Golias, I. (2002). Effects of road geometry and traffic volumes on rural roadway accident rates. Accident; Analysis and Prevention, 34(3), 357–365.KarlaftisM. G.GoliasI.2002Effects of road geometry and traffic volumes on rural roadway accident rates34335736510.1016/S0001-4575(01)00033-1Search in Google Scholar
Kolyshkina, I., Wong, S., & Lim, S. (2004). Enhancing generalised linear models with data mining. In Casualty Actuarial Society (pp. 279–290).KolyshkinaI.WongS.LimS.2004Enhancing generalised linear models with data miningIn279290Search in Google Scholar
Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574–582.LakhaniP.SundaramB.2017Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks284257458210.1148/radiol.2017162326Search in Google Scholar
Levenson, R. M., Krupinski, E. A., Navarro, V. M., & Wasserman, E. A. (2015). Pigeons (Columba livia) as trainable observers of pathology and radiology breast cancer images. PloS One, 10(11), e0141357.LevensonR. M.KrupinskiE. A.NavarroV. M.WassermanE. A.2015Pigeons (Columba livia) as trainable observers of pathology and radiology breast cancer images1011e014135710.1371/journal.pone.0141357Search in Google Scholar
Lorenz, M. O. (1905). Methods of measuring the concentration of wealth. Publications of the American Statistical Association, 9(70), 209–219.LorenzM. O.1905Methods of measuring the concentration of wealth97020921910.2307/2276207Search in Google Scholar
McCartt, A. T., Shabanova, V. I., & Leaf, W. A. (2003). Driving experience, crashes and traffic citations of teenage beginning drivers. Accident; Analysis and Prevention, 35(3), 311–320.McCarttA. T.ShabanovaV. I.LeafW. A.2003Driving experience, crashes and traffic citations of teenage beginning drivers35331132010.1016/S0001-4575(02)00006-4Search in Google Scholar
Rolison, J. J., Hanoch, Y., Wood, S., & Liu, P.-J. (2014). Risk-taking differences across the adult life span: A question of age and domain. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 69(6), 870–880.RolisonJ. J.HanochY.WoodS.LiuP.-J.2014Risk-taking differences across the adult life span: A question of age and domain69687088010.1093/geronb/gbt081Search in Google Scholar
Shankar, V., Mannering, F., & Barfield, W. (1995). Effect of roadway geometrics and environmental factors on rural freeway accident frequencies. Accident; Analysis and Prevention, 27(3), 371–389.ShankarV.ManneringF.BarfieldW.1995Effect of roadway geometrics and environmental factors on rural freeway accident frequencies27337138910.1016/0001-4575(94)00078-ZSearch in Google Scholar
Spedicato, G. A., Dutang, C., & Petrini, L. (2018). Machine learning methods to perform pricing optimization. A comparison with standard GLMs. Variance: Advancing the Science of Risk, 111(2), 69–89.SpedicatoG. A.DutangC.PetriniL.2018Machine learning methods to perform pricing optimization. A comparison with standard GLMs11126989Search in Google Scholar
Spilkova, J., Dzúrova, D., & Pitonak, M. (2014). Perception of neighborhood environment and health risk behaviors in Prague’s teenagers: A pilot study in a post-communist city. International Journal of Health Geographics, 13, 41.SpilkovaJ.DzúrovaD.PitonakM.2014Perception of neighborhood environment and health risk behaviors in Prague’s teenagers: A pilot study in a post-communist city134110.1186/1476-072X-13-41Search in Google Scholar
Strayer, D. L., Drews, F. A., & Crouch, D. J. (2003). Fatal distraction? A comparison of the cell-phone driver and the drunk driver. In Driving Assessment Conference (Vol. 2). University of Iowa. doi: 10.17077/drivingassessment.1085.StrayerD. L.DrewsF. A.CrouchD. J.2003Fatal distraction? A comparison of the cell-phone driver and the drunk driverIn2University of Iowa10.17077/drivingassessment.1085Open DOISearch in Google Scholar
Taylor, G. (2001). Geographic premium rating by whittaker spatial smoothing. ASTIN Bulletin: The Journal of the IAA, 31(1), 147–160.TaylorG.2001Geographic premium rating by whittaker spatial smoothing31114716010.2143/AST.31.1.999Search in Google Scholar
Tran-Thanh, L., Stein, S., Rogers, A., & Jennings, N. R. (2014). Efficient crowdsourcing of unknown experts using bounded multi-armed bandits. Artificial Intelligence, 214, 89–111.Tran-ThanhL.SteinS.RogersA.JenningsN. R.2014Efficient crowdsourcing of unknown experts using bounded multi-armed bandits2148911110.1016/j.artint.2014.04.005Search in Google Scholar
Werner, G., & Modlin, C. (2016). Basic ratemaking (5 ed.). Casualty Actuarial Society.WernerG.ModlinC.20165 edCasualty Actuarial SocietySearch in Google Scholar
Yan, J., Guszcza, J., Flynn, M., & Wu, C.-S. P. (2009). Applications of the offset in property-casualty predictive modeling. In Casualty Actuarial Society E-Forum, Winter 2009 (p. 366).YanJ.GuszczaJ.FlynnM.WuC.-S. P.2009Applications of the offset in property-casualty predictive modelingInWinter2009366Search in Google Scholar
Yao, J. (2008). Clustering in ratemaking: Applications in territories clustering. Casualty Actuarial Society Discussion Paper Program Casualty Actuarial Society-Arlington, Virginia, 170–192.YaoJ.2008Clustering in ratemaking: Applications in territories clustering170192Search in Google Scholar
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., & Oliva, A. (2014). Learning deep features for scene recognition using places database. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in neural information processing systems 27 (pp. 487–495). Red Hook, NY: Curran Associates.ZhouB.LapedrizaA.XiaoJ.TorralbaA.OlivaA.2014Learning deep features for scene recognition using places databaseInGhahramaniZ.WellingM.CortesC.LawrenceN. D.WeinbergerK. Q.(Eds.),487495Red Hook, NYCurran AssociatesSearch in Google Scholar