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Regression Modeling with Actuarial and Financial Applications (International Series on Actuarial Science), by Edward W. Frees
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Statistical techniques can be used to address new situations. This is important in a rapidly evolving risk management and financial world. Analysts with a strong statistical background understand that a large data set can represent a treasure trove of information to be mined and can yield a strong competitive advantage. This book provides budding actuaries and financial analysts with a foundation in multiple regression and time series. Readers will learn about these statistical techniques using data on the demand for insurance, lottery sales, foreign exchange rates, and other applications. Although no specific knowledge of risk management or finance is presumed, the approach introduces applications in which statistical techniques can be used to analyze real data of interest. In addition to the fundamentals, this book describes several advanced statistical topics that are particularly relevant to actuarial and financial practice, including the analysis of longitudinal, two-part (frequency/severity), and fat-tailed data. Datasets with detailed descriptions, sample statistical software scripts in "R" and "SAS," and tips on writing a statistical report, including sample projects, can be found on the book's Web site: http://research.bus.wisc.edu/RegActuaries.
- Sales Rank: #821180 in Books
- Brand: Brand: Cambridge University Press
- Published on: 2009-11-30
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x 1.18" w x 6.85" l, 2.10 pounds
- Binding: Paperback
- 584 pages
- Used Book in Good Condition
Review
"This is an excellent book written by an all-round writer. He is a Fellow of both the Society of Actuaries and the American Statistical Association. Hence, it is not surprising that the book fills the gap between modern statistics and traditional actuarial/risk management methods. Need for this kind of book is obvious. I highly recommend it to any person who wishes to learn how to use statistical methods for actuarial and financial applications.
Lasse Koskinen, International Statistical Review
"The author provides an outstanding list of references at the end of the chapters that provides additional reading on the various topics. In addition the author provides programs in SAS and R as well as output form these packages."
Michael R. Chernick, Significance Magazine
"a welcome addition to the bookshelf of practicing actuaries at all levels, both actuarial students charged with conducting analyses for which the methods discussed in the book are most relevant, and senior managers who use such analyses as a basis for financial decision-making. Perhaps my favorite part of Frees's book is the final two chapters, on Report Writing and Designing Effective Graphs. If these fine essays do not already appear somewhere on the Society of Actuaries syllabus, they should be added immediately."
Ronald C. NEATH, The American Statistician
About the Author
Edward W. (Jed) Frees is a Professor of Business at the University of Wisconsin, Madison and is holder of the Assurant Health Insurance Professorship of Actuarial Science. He is a Fellow of both the Society of Actuaries (SoA) and the American Statistical Association (ASA). Professor Frees is the author of Longitudinal and Panel Data (2004) and has published more than fifty articles in leading refereed academic journals.
Most helpful customer reviews
6 of 6 people found the following review helpful.
regression and survival analysis with apps strickly for actuaries and financial analyst
By Michael R. Chernick
This text covers regression techniques which the author views as the most commonly used statistical technique especially in the world of insurance and finance. Since the book is in a series for actuarial science I expected the presentation to be elementary to intermediate and to only cover regression. But some of the latter topics were quite advanced. In my mind survival analysis which is extremely important to actuaries and time series analysis which in very important to finance would not be covered as they do not fall into the category of regression. But thankfully they are included.
Some time series models such as polynomial functions of time can be viewed as linear regression models where time is the predictor variable for the response. But the main models like exponetial smoothing, Box-Jenkins and ARCH/GARCH which are the main ones applied in financial forecasting are really not regression in my view. But these too are covered in the book.
The book starts out in Chapter 1 with a very elementary review of statistics and simple forms of regression. Then Chapter 2-6 form part I which is titled Regression. Chapter 2 presents the basics of simple linear regression. Chapters 3 and 4 cover multiple regression. This is not a cookbook of techniques. The author provides background, historical developments and important concepts and mixes in applications to actuarial science and finance throughout. At the end of most chapters are a large number of exercises with solutions for selected problems in the back. In chapter 3 the author explains least squares presents the modeling assumptions and introduces the Gauss-Markovas well as all the standard concepts of hypothesis testing that a regression parameter is significant, R-square and theorem (hence also the concept of minimum variance among unbiased estimators). In Chapter 4 he provides the unified theme of the general linear hypothesis as he covers categorical predictor variables, the analysis of variance and covariance (all general linear models) In Chapter 5, leverage points, multicollinearity, and regression diagnostics are presented in the context of variable selection. Chapter 6 is all about interpretation and limitations.
Later in Parts III and IV the author introduces nonlinear regression models, logistic regression, probit and tobit models, Poisson and negative binomial regression, generalized linear models,and specialized techniques such as bootstrapping, mixed linear models, proportional hazards regression, generalized additive models and the Bayesian approach to regression. The coverage gets more advanced as you move through the chapters
Part II on time series includes seasonal models, discussion of stationary and longitudinal and panel data models.
In Part III survival analysis is included in Chapter 14. This includes the Kaplan-Meier estimates, proportional hazards regression, accelerated failure time models and even the analysis of recurrent events.
Part IV specifically focuses on actuarial applications and it is here that heavytailed distributions are dealt with using quantile regression and extreme value probability models.
With such an extensive list of topics the book is a large volume of over 560 pages. But even so it is not possible to do justice to this extensive list. The author provides an outstanding list of references at the end of the chapters that provides additional reading on the various topics.
In addition the author prvides programs in SAS and R as well as output form these packages. More detailed examples and projects can be found on the books website.
0 of 0 people found the following review helpful.
Great Introduction
By Antonio Cecilio Miranda Tejeida
I believe this one is a good way to move from theory with a little review of it into practice, since most of owr training is in univariate and most of the time we are not shown how its done outside, I also recommend the book
Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques (International Series on Actuarial Science) from the same author eventhough he is the editor
0 of 0 people found the following review helpful.
Five Stars
By Gee Yul Lee
Highly recommended book on predictive modeling. It is a must have for both practicing actuaries and academic researchers.
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