Predictive Modeling Applications In Actuarial Science
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Predictive Modeling Applications in Actuarial Science by Edward W. Frees,Richard A. Derrig,Glenn Meyers Pdf
This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.
Predictive Modeling Applications in Actuarial Science: Volume 2, Case Studies in Insurance by Edward W. Frees,Glenn Meyers,Richard A. Derrig Pdf
Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes. Forecasting financial events is a core skill that actuaries routinely apply in insurance and other risk-management applications. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used to gain a competitive advantage in situations with complex data. Volume 2 examines applications of predictive modeling. Where Volume 1 developed the foundations of predictive modeling, Volume 2 explores practical uses for techniques, focusing on property and casualty insurance. Readers are exposed to a variety of techniques in concrete, real-life contexts that demonstrate their value and the overall value of predictive modeling, for seasoned practicing analysts as well as those just starting out.
Predictive Modeling Applications in Actuarial Science: Volume 2, Case Studies in Insurance by Edward W. Frees,Glenn Meyers,Richard A. Derrig Pdf
Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes. Forecasting financial events is a core skill that actuaries routinely apply in insurance and other risk-management applications. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used to gain a competitive advantage in situations with complex data. Volume 2 examines applications of predictive modeling. Where Volume 1 developed the foundations of predictive modeling, Volume 2 explores practical uses for techniques, focusing on property and casualty insurance. Readers are exposed to a variety of techniques in concrete, real-life contexts that demonstrate their value and the overall value of predictive modeling, for seasoned practicing analysts as well as those just starting out.
Predictive Modeling Applications in Actuarial Science by Anonim Pdf
"The International Series on Actuarial Science, published by Cambridge University Press in con-junction with the Institute and Faculty of Actuaries, contains textbooks for students taking courses in or related to actuarial science, as well as more advanced works designed for continuing pro-fessional development or for describing and synthesizing research. The series is a vehicle for publishing books that reflect changes and developments in the curriculum, that encourage the introduction of courses on actuarial science in universities, and that show how actuarial science can be used in all areas where there is long-term financial risk"--
David C. M. Dickson,Mary R. Hardy,Howard R. Waters
Author : David C. M. Dickson,Mary R. Hardy,Howard R. Waters Publisher : Cambridge University Press Page : 180 pages File Size : 48,7 Mb Release : 2012-03-26 Category : Business & Economics ISBN : 9781107608443
Solutions Manual for Actuarial Mathematics for Life Contingent Risks by David C. M. Dickson,Mary R. Hardy,Howard R. Waters Pdf
"This manual presents solutions to all exercises from Actuarial Mathematics for Life Contingent Risks (AMLCR) by David C.M. Dickson, Mary R. Hardy, Howard Waters; Cambridge University Press, 2009. ISBN 9780521118255"--Pref.
Author : Max Kuhn,Kjell Johnson Publisher : Springer Science & Business Media Page : 600 pages File Size : 52,9 Mb Release : 2013-05-17 Category : Medical ISBN : 9781461468493
Applied Predictive Modeling by Max Kuhn,Kjell Johnson Pdf
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Computational Actuarial Science with R by Arthur Charpentier Pdf
A Hands-On Approach to Understanding and Using Actuarial ModelsComputational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/
Generalized Linear Models for Insurance Data by Piet de Jong,Gillian Z. Heller Pdf
This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.
Healthcare Risk Adjustment and Predictive Modeling by Ian G. Duncan Pdf
This text is listed on the Course of Reading for SOA Fellowship study in the Group & Health specialty track. Healthcare Risk Adjustment and Predictive Modeling provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare, disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides access to a test dataset.
Systems Science and Modeling for Ecological Economics by Alexey A. Voinov Pdf
Modeling is a key component to sciences from mathematics to life science, including environmental and ecological studies. By looking at the underlying concepts of the software, we can make sure that we build mathematically feasible models and that we get the most out of the data and information that we have. Systems Science and Modeling for Ecological Economics shows how models can be analyzed using simple math and software to generate meaningful qualitative descriptions of system dynamics. This book shows that even without a full analytical, mathematically rigorous analysis of the equations, there may be ways to derive some qualitative understanding of the general behavior of a system. By relating some of the modeling approaches and systems theory to real-world examples the book illustrates how these approaches can help understand concepts such as sustainability, peak oil, adaptive management, optimal harvest and other practical applications. Relates modeling approaches and systems theory to real-world examples Teaches students to build mathematically feasible models and get the most out of the data and information available Wide range of applications in hydrology, population dynamics, market cycles, sustainability theory, management, and more
