Regression Modeling with Actuarial and Financial Applications


Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 1139484931
Category: Business & Economics
Page: 584
View: 6576

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This text gives budding actuaries and financial analysts a foundation in multiple regression and time series. They 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.

Regression Modeling with Actuarial and Financial Applications


Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 9780521135962
Category: Business & Economics
Page: 584
View: 3959

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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.

Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques


Author: Edward W. Frees,Richard A. Derrig,Glenn Meyers
Publisher: Cambridge University Press
ISBN: 1139992317
Category: Business & Economics
Page: N.A
View: 7126

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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.

Actuarial Mathematics for Life Contingent Risks


Author: David C. M. Dickson,Mary Hardy,Mary R. Hardy,Howard R. Waters
Publisher: Cambridge University Press
ISBN: 1107044073
Category: Business & Economics
Page: 616
View: 4012

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This groundbreaking text has been augmented with new material and fully updated to prepare students for the new-style MLC exam.

Generalized Linear Models for Insurance Data


Author: Piet de Jong,Gillian Z. Heller
Publisher: Cambridge University Press
ISBN: 1139470477
Category: Business & Economics
Page: N.A
View: 8505

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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.

Predictive Modeling Applications in Actuarial Science: Volume 2, Case Studies in Insurance


Author: Edward W. Frees,Glenn Meyers,Richard A. Derrig
Publisher: Cambridge University Press
ISBN: 1316720527
Category: Business & Economics
Page: N.A
View: 9720

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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.

Modelling Mortality with Actuarial Applications


Author: Angus S. Macdonald,Stephen J. Richards,Iain D. Currie
Publisher: Cambridge University Press
ISBN: 110704541X
Category: Business & Economics
Page: 385
View: 9788

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Modern mortality modelling for actuaries and actuarial students, with example R code, to unlock the potential of individual data.

Financial Enterprise Risk Management


Author: Paul Sweeting
Publisher: Cambridge University Press
ISBN: 1107184614
Category: Business & Economics
Page: 600
View: 7333

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This comprehensive, yet accessible, guide to enterprise risk management for financial institutions contains all the tools needed to build and maintain an ERM framework. It discusses the internal and external contexts with which risk management must be carried out, and it covers a range of qualitative and quantitative techniques that can be used to identify, model and measure risks. This new edition has been thoroughly updated to reflect new legislation and the creation of the Financial Conduct Authority and the Prudential Regulation Authority. It includes new content on Bayesian networks, expanded coverage of Basel III, a revised treatment of operational risk and a fully revised index. Over 100 diagrams are used to illustrate the range of approaches available, and risk management issues are highlighted with numerous case studies. This book also forms part of the core reading for the UK actuarial profession's specialist technical examination in enterprise risk management, ST9.

Computational Actuarial Science with R


Author: Arthur Charpentier
Publisher: CRC Press
ISBN: 1498759823
Category: Business & Economics
Page: 656
View: 9825

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A Hands-On Approach to Understanding and Using Actuarial Models Computational 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/C++ embedded codes. After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance. Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).

Solutions Manual for Actuarial Mathematics for Life Contingent Risks


Author: David C. M. Dickson,Mary R. Hardy,Howard R. Waters
Publisher: Cambridge University Press
ISBN: 1107620260
Category: Business & Economics
Page: 227
View: 4397

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This must-have manual provides detailed solutions to all of the 200+ exercises in Dickson, Hardy and Waters' Actuarial Mathematics for Life Contingent Risks, Second Edition. This groundbreaking text on the modern mathematics of life insurance is required reading for the Society of Actuaries' Exam MLC and also provides a solid preparation for the life contingencies material of the UK actuarial profession's exam CT5. Beyond the professional examinations, the textbook and solutions manual offer readers the opportunity to develop insight and understanding, and also offer practical advice for solving problems using straightforward, intuitive numerical methods. Companion spreadsheets illustrating these techniques are available for free download.

R for Everyone

Advanced Analytics and Graphics
Author: Jared P. Lander
Publisher: Addison-Wesley Professional
ISBN: 0134546997
Category: Computers
Page: 560
View: 3353

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Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.

Non-Life Insurance Pricing with Generalized Linear Models


Author: Esbjörn Ohlsson,Björn Johansson
Publisher: Springer Science & Business Media
ISBN: 9783642107917
Category: Mathematics
Page: 174
View: 9034

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Non-life insurance pricing is the art of setting the price of an insurance policy, taking into consideration varoius properties of the insured object and the policy holder. Introduced by British actuaries generalized linear models (GLMs) have become today a the standard aproach for tariff analysis. The book focuses on methods based on GLMs that have been found useful in actuarial practice and provides a set of tools for a tariff analysis. Basic theory of GLMs in a tariff analysis setting is presented with useful extensions of standarde GLM theory that are not in common use. The book meets the European Core Syllabus for actuarial education and is written for actuarial students as well as practicing actuaries. To support reader real data of some complexity are provided at www.math.su.se/GLMbook.

Statistical and Probabilistic Methods in Actuarial Science


Author: Philip J. Boland
Publisher: CRC Press
ISBN: 158488696X
Category: Mathematics
Page: 368
View: 9994

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Statistical and Probabilistic Methods in Actuarial Science covers many of the diverse methods in applied probability and statistics for students aspiring to careers in insurance, actuarial science, and finance. The book builds on students’ existing knowledge of probability and statistics by establishing a solid and thorough understanding of these methods. It also emphasizes the wide variety of practical situations in insurance and actuarial science where these techniques may be used. Although some chapters are linked, several can be studied independently from the others. The first chapter introduces claims reserving via the deterministic chain ladder technique. The next few chapters survey loss distributions, risk models in a fixed period of time, and surplus processes, followed by an examination of credibility theory in which collateral and sample information are brought together to provide reasonable methods of estimation. In the subsequent chapter, experience rating via no claim discount schemes for motor insurance provides an interesting application of Markov chain methods. The final chapters discuss generalized linear models and decision and game theory. Developed by an author with many years of teaching experience, this text presents an accessible, sound foundation in both the theory and applications of actuarial science. It encourages students to use the statistical software package R to check examples and solve problems.

Longitudinal and Panel Data

Analysis and Applications in the Social Sciences
Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 9780521535380
Category: Business & Economics
Page: 467
View: 4067

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An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.

An Introduction to Statistical Learning

with Applications in R
Author: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
Publisher: Springer Science & Business Media
ISBN: 1461471389
Category: Mathematics
Page: 426
View: 2453

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Financial and Actuarial Statistics

An Introduction, Second Edition
Author: Dale S. Borowiak,Arnold F. Shapiro
Publisher: CRC Press
ISBN: 0203911245
Category: Mathematics
Page: 392
View: 5875

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Understand Up-to-Date Statistical Techniques for Financial and Actuarial Applications Since the first edition was published, statistical techniques, such as reliability measurement, simulation, regression, and Markov chain modeling, have become more prominent in the financial and actuarial industries. Consequently, practitioners and students must acquire strong mathematical and statistical backgrounds in order to have successful careers. Financial and Actuarial Statistics: An Introduction, Second Edition enables readers to obtain the necessary mathematical and statistical background. It also advances the application and theory of statistics in modern financial and actuarial modeling. Like its predecessor, this second edition considers financial and actuarial modeling from a statistical point of view while adding a substantial amount of new material. New to the Second Edition Nomenclature and notations standard to the actuarial field Excel exercises with solutions, which demonstrate how to use Excel functions for statistical and actuarial computations Problems dealing with standard probability and statistics theory, along with detailed equation links A chapter on Markov chains and actuarial applications Expanded discussions of simulation techniques and applications, such as investment pricing Sections on the maximum likelihood approach to parameter estimation as well as asymptotic applications Discussions of diagnostic procedures for nonnegative random variables and Pareto, lognormal, Weibull, and left truncated distributions Expanded material on surplus models and ruin computations Discussions of nonparametric prediction intervals, option pricing diagnostics, variance of the loss function associated with standard actuarial models, and Gompertz and Makeham distributions Sections on the concept of actuarial statistics for a collection of stochastic status models The book presents a unified approach to both financial and actuarial modeling through the use of general status structures. The authors define future time-dependent financial actions in terms of a status structure that may be either deterministic or stochastic. They show how deterministic status structures lead to classical interest and annuity models, investment pricing models, and aggregate claim models. They also employ stochastic status structures to develop financial and actuarial models, such as surplus models, life insurance, and life annuity models.

The Handbook of Graph Algorithms and Applications

Applications
Author: Krishnaiyan Thulasiraman,Arun Kumar Somani,Sarma Vrudhula
Publisher: CRC Press
ISBN: 1482227061
Category: Mathematics
Page: 1024
View: 3915

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The Handbook of Graph Algorithms, Volume II : Applications focuses on a wide range of algorithmic applications, including graph theory problems. The book emphasizes new algorithms and approaches that have been triggered by applications. The approaches discussed require minimal exposure to related technologies in order to understand the material. Each chapter is devoted to a single application area, from VLSI circuits to optical networks to program graphs, and features an introduction by a pioneer researcher in that particular field. The book serves as a single-source reference for graph algorithms and their related applications.

Reinsurance

Actuarial and Statistical Aspects
Author: Hansjöerg Albrecher,Jan Beirlant,Jozef L. Teugels
Publisher: John Wiley & Sons
ISBN: 1119419948
Category: Mathematics
Page: 368
View: 5503

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Reinsurance: Actuarial and Statistical Aspects provides a survey of both the academic literature in the field as well as challenges appearing in reinsurance practice and puts the two in perspective. The book is written for researchers with an interest in reinsurance problems, for graduate students with a basic knowledge of probability and statistics as well as for reinsurance practitioners. The focus of the book is on modelling together with the statistical challenges that go along with it. The discussed statistical approaches are illustrated alongside six case studies of insurance loss data sets, ranging from MTPL over fire to storm and flood loss data. Some of the presented material also contains new results that have not yet been published in the research literature. An extensive bibliography provides readers with links for further study.