Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Martin Spindler"'
Autor:
Ivan Fursov, Elizaveta Kovtun, Rodrigo Rivera-Castro, Alexey Zaytsev, Rasul Khasyanov, Martin Spindler, Evgeny Burnaev
Publikováno v:
IEEE Access, Vol 10, Pp 32060-32074 (2022)
Roughly 10 percent of the insurance industry’s incurred losses are estimated to stem from fraudulent claims. One solution is to use tabular data to construct models that can distinguish between claims that are legitimate and those that are fraudule
Externí odkaz:
https://doaj.org/article/ad169490f0034ee494bdac945425080d
Publikováno v:
Journal of Econometrics. 228:244-258
Insurance companies must manage millions of claims per year. While most of these are not fraudulent, those that are nevertheless cost insurance companies and those they insure vast amounts of money. The ultimate goal is to develop a predictive model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b65ed8eb8763a14733e4c63c397956c7
https://doi.org/10.1016/j.jeconom.2020.05.021
https://doi.org/10.1016/j.jeconom.2020.05.021
Publikováno v:
The Econometrics Journal. 25:277-300
This paper combines causal mediation analysis with double machine learning to control for observed confounders in a data-driven way under a selection-on-observables assumption in a high-dimensional setting. We consider the average indirect effect of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d302654eed786c2ddac643b3f08c7cd
https://doi.org/10.1093/ectj/utac003
https://doi.org/10.1093/ectj/utac003
Publikováno v:
Work and AI 2030 ISBN: 9783658402310
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::224b13cbb01e0a3a3e5a64dd09043d72
https://doi.org/10.1007/978-3-658-40232-7_18
https://doi.org/10.1007/978-3-658-40232-7_18
Publikováno v:
Journal of Business & Economic Statistics. 40:1168-1178
Transformation models are a very important tool for applied statisticians and econometricians. In many applications, the dependent variable is transformed so that homogeneity or normal distribution of the error holds. In this article, we analyze tran
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::083df0edca20fa27773ff6a333fab555
https://doi.org/10.1080/07350015.2021.1906259
https://doi.org/10.1080/07350015.2021.1906259
Autor:
Cristina Sattarhoff, Martin Spindler
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b42fb8d9dce0fae70c1f57a85989df9
https://doi.org/10.2139/ssrn.4296969
https://doi.org/10.2139/ssrn.4296969
Publikováno v:
Arbeitswelt und KI 2030 ISBN: 9783658357788
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a8e8c3e53a149f48239c169cd65b01d4
https://doi.org/10.1007/978-3-658-35779-5_18
https://doi.org/10.1007/978-3-658-35779-5_18
Autor:
Helmut Wasserbacher, Martin Spindler
This article is an introduction to machine learning for financial forecasting, planning and analysis (FP\&A). Machine learning appears well suited to support FP\&A with the highly automated extraction of information from large amounts of data. Howeve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e6f646e3804f67477c2d89380ce09da
Publikováno v:
Philipp Bach
DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models. It contains functionalities for valid statistical inference on causal parameters when the estim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a85807a02c5a5910291bd89743dc20c
Publikováno v:
Social Science Computer Review
OnlineFirst
OnlineFirst
Survey scientists increasingly face the problem of high-dimensionality in their research as digitization makes it much easier to construct high-dimensional (or "big") data sets through tools such as online surveys and mobile applications. Machine lea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::825a1f76b679ff521275389603b4a33e
https://doi.org/10.1177/08944393221095194
https://doi.org/10.1177/08944393221095194