Actuarial Exam Tactics
Author: Mike Jennings
Publisher:
Published: 2017
Total Pages:
ISBN-13: 9781635880397
DOWNLOAD EBOOK →Author: Mike Jennings
Publisher:
Published: 2017
Total Pages:
ISBN-13: 9781635880397
DOWNLOAD EBOOK →Author: Michel Denuit
Publisher: Springer Nature
Published: 2020-11-16
Total Pages: 228
ISBN-13: 303057556X
DOWNLOAD EBOOK →This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, master's students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful. This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance.
Author: Michel Denuit
Publisher: Springer Nature
Published: 2019-09-03
Total Pages: 441
ISBN-13: 3030258203
DOWNLOAD EBOOK →This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.
Author: Mario V. Wüthrich
Publisher: Springer Nature
Published: 2022-11-22
Total Pages: 611
ISBN-13: 303112409X
DOWNLOAD EBOOK →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.
Author: Edward W. Frees
Publisher: Cambridge University Press
Published: 2010
Total Pages: 585
ISBN-13: 0521760119
DOWNLOAD EBOOK →This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Author: Samuel A. Broverman
Publisher:
Published: 2004
Total Pages:
ISBN-13: 9781566985024
DOWNLOAD EBOOK →Author: Michel Denuit
Publisher:
Published: 2019
Total Pages: 441
ISBN-13: 9783030258214
DOWNLOAD EBOOK →This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P & C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.
Author: Nicholas Mocciolo
Publisher:
Published: 2018-03
Total Pages:
ISBN-13: 9781635883756
DOWNLOAD EBOOK →Print version
Author: United States. Social Security Administration. Office of the Actuary
Publisher:
Published: 1937
Total Pages: 558
ISBN-13:
DOWNLOAD EBOOK →