Predictive Modeling Applications in Actuarial Science

doi:10.1017/CBO9781139342674

Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.

'With contributions coming from a wide variety of researchers, professors, and actuaries - including several CAS Fellows - it’s clear that this book will be valuable for any P and C actuary whose main concern is using predictive modeling in his or her own work.'

David Zornek Source: Actuarial Review

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Contents

Frontmatter
pp i-vi
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Contents
pp vii-xii
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Contributor List
pp xiii-xviii
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Acknowledgments
pp xix-xx
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1 - Predictive Modeling in Actuarial Science
pp 1-10
- By Edward W. Frees, University of Wisconsin-Madison, Richard A. Derrig, Temple University, Philadelphia, Glenn Meyers
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II - Predictive Modeling Foundations
pp 11-12
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2 - Overview of Linear Models
pp 13-64
- By Marjorie Rosenberg, University of Wisconsin-Madison, James Guszcza, University of Wisconsin-Madison
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3 - Regression with Categorical Dependent Variables
pp 65-86
- By Montserrat Guillén, University of Barcelona
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4 - Regression with Count-Dependent Variables
pp 87-106
- By Jean-Philippe Boucher, Université du Québec à Montréal (UQAM)
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5 - Generalized Linear Models
pp 107-137
- By Curtis Gary Dean, Ball State University
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6 - Frequency and Severity Models
pp 138-164
- By Edward W. Frees, University of Wisconsin-Madison
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II - Predictive Modeling Methods
pp 165-166
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7 - Longitudinal and Panel Data Models
pp 167-181
- By Edward W. Frees, University of Wisconsin-Madison
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8 - Linear Mixed Models
pp 182-216
- By Katrien Antonio, University of Amsterdam and KU Leuven, Yanwei Zhang, University of Southern California
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9 - Credibility and Regression Modeling
pp 217-235
- By Vytaras Brazauskas, University of Wisconsin-Milwaukee, Harald Dornheim, University of Wisconsin-Milwaukee, Ponmalar Ratnam, University of Wisconsin-Milwaukee
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10 - Fat-Tailed Regression Models
pp 236-259
- By Peng Shi, University of Wisconsin-Madison
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11 - Spatial Modeling
pp 260-279
- By Eike Brechmann, Technische Universität München, Claudia Czado, Technische Universitat in Munich
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12 - Unsupervised Learning
pp 280-312
- By Louise Francis
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III - Bayesian and Mixed Modeling
pp 313-314
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13 - Bayesian Computational Methods
pp 315-333
- By Brian Hartman, University of Connecticut
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14 - Bayesian Regression Models
pp 334-366
- By Luis E. Nieto-Barajas, Instituto Tecnológico Autónomo de México, Enrique de Alba, Instituto Tecnológico Autónomo de México (ITAM)
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15 - Generalized Additive Models and Nonparametric Regression
pp 367-397
- By Patrick L. Brockett, University of Texas at Austin, Shuo-Li Chuang, The University of Texas at Austin, Utai Pitaktong, The University of Texas at Austin
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16 - Nonlinear Mixed Models
pp 398-424
- By Katrien Antonio, University of Amsterdam and KU Leuven, Yanwei Zhang, University of Southern California
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IV - Longitudinal Modeling
pp 425-426
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17 - Time Series Analysis
pp 427-448
- By Piet de Jong, Macquarie University in Sydney, Australia
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18 - Claims Triangles/Loss Reserves
pp 449-480
- By Greg Taylor, University of New South Wales
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19 - Survival Models
pp 481-514
- By Jim Robinson, University of Wisconsin-Madison
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20 - Transition Modeling
pp 515-538
- By Bruce Jones, University of Western Ontario, Weijia Wu, University of Southern California
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Index
pp 539-543
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