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Romtrade Corp., STERLING HEIGHTS, MI, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Vendedor de AbeBooks desde 17 de abril de 2013
This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. N° de ref. del artículo ABNR-277542
Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features.
Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.
Acerca del autor:
Mario Wüthrich is Professor in the Department of Mathematics at ETH Zurich, Honorary Visiting Professor at City, University of London (2011-2022), Honorary Professor at University College London (2013-2019), and Adjunct Professor at University of Bologna (2014-2016). He holds a Ph.D. in Mathematics from ETH Zurich (1999). From 2000 to 2005, he held an actuarial position at Winterthur Insurance, Switzerland. He is Actuary SAA (2004), served on the board of the Swiss Association of Actuaries (2006-2018), and is Editor-in-Chief of ASTIN Bulletin (since 2018).
Michael Merz has been the holder of the Chair of Mathematics and Statistics in Economics at the University of Hamburg since 2009. After completing his doctorate at the University of Tübingen on a topic from the field of risk theory, he worked from 2004 to 2006 in the actuarial department of Baloise Insurance Group and gained practical experience in the areas of quantitative risk management and Actuarial Science. He then worked until 2009 as a Juniorprofessor for statistics, risk and insurance at the University of Tübingen. Since the beginning of 2018 he is editor of ASTIN Bulletin.
Título: STATISTICAL FOUNDATIONS OF ACTUARIAL ...
Editorial: Springer
Año de publicación: 2022
Encuadernación: Encuadernación de tapa blanda
Condición: New
Librería: medimops, Berlin, Alemania
Condición: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages. Nº de ref. del artículo: M03031124111-V
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Librería: Chiron Media, Wallingford, Reino Unido
PF. Condición: New. Nº de ref. del artículo: 6666-IUK-9783031124112
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: Majestic Books, Hounslow, Reino Unido
Condición: New. Nº de ref. del artículo: 402212591
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Librería: Books Puddle, New York, NY, Estados Unidos de America
Condición: New. Nº de ref. del artículo: 26395245872
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Librería: Rarewaves.com UK, London, Reino Unido
Paperback. Condición: New. 1st ed. 2023. This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus. Nº de ref. del artículo: LU-9783031124112
Cantidad disponible: Más de 20 disponibles