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Author: Yanwei Zhang
Publisher: Chapman & Hall/CRC
ISBN: 9781466510616
Category : Business & Economics
Languages : en
Pages : 320
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Book Description
There has been a rapidly growing interest in Bayesian methods among insurance practitioners in recent years, mainly because of their ability to generate predictive distributions and to rigorously incorporate expert opinion through prior probabilities. This book introduces modern Bayesian modeling techniques for actuarial and insurance applications. It first provides the necessary background in current actuarial practice and then presents Bayesian methods and MCMC. It includes advanced techniques, such as nonlinear modeling, as well as three chapters on model selection and averaging. The text features case studies using real actuarial and insurance data with computations in R and WinBUGS.
Author: Yanwei Zhang
Publisher: Chapman & Hall/CRC
ISBN: 9781466510616
Category : Business & Economics
Languages : en
Pages : 320
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Book Description
There has been a rapidly growing interest in Bayesian methods among insurance practitioners in recent years, mainly because of their ability to generate predictive distributions and to rigorously incorporate expert opinion through prior probabilities. This book introduces modern Bayesian modeling techniques for actuarial and insurance applications. It first provides the necessary background in current actuarial practice and then presents Bayesian methods and MCMC. It includes advanced techniques, such as nonlinear modeling, as well as three chapters on model selection and averaging. The text features case studies using real actuarial and insurance data with computations in R and WinBUGS.
Author: Stuart A. Klugman
Publisher: Springer Science & Business Media
ISBN: 9401708452
Category : Business & Economics
Languages : en
Pages : 242
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Book Description
The debate between the proponents of "classical" and "Bayesian" statistica} methods continues unabated. It is not the purpose of the text to resolve those issues but rather to demonstrate that within the realm of actuarial science there are a number of problems that are particularly suited for Bayesian analysis. This has been apparent to actuaries for a long time, but the lack of adequate computing power and appropriate algorithms had led to the use of various approximations. The two greatest advantages to the actuary of the Bayesian approach are that the method is independent of the model and that interval estimates are as easy to obtain as point estimates. The former attribute means that once one learns how to analyze one problem, the solution to similar, but more complex, problems will be no more difficult. The second one takes on added significance as the actuary of today is expected to provide evidence concerning the quality of any estimates. While the examples are all actuarial in nature, the methods discussed are applicable to any structured estimation problem. In particular, statisticians will recognize that the basic credibility problem has the same setting as the random effects model from analysis of variance.
Author: Guangyuan Gao
Publisher: Springer
ISBN: 9811336091
Category : Mathematics
Languages : en
Pages : 205
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Book Description
This book first provides a review of various aspects of Bayesian statistics. It then investigates three types of claims reserving models in the Bayesian framework: chain ladder models, basis expansion models involving a tail factor, and multivariate copula models. For the Bayesian inferential methods, this book largely relies on Stan, a specialized software environment which applies Hamiltonian Monte Carlo method and variational Bayes.
Author: Philip J. Boland
Publisher: CRC Press
ISBN: 158488696X
Category : Business & Economics
Languages : en
Pages : 368
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Book Description
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
Author: Yanwei Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 22
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Book Description
While Bayesian methods have attracted considerable interest in actuarial science, they are yet to be embraced in large-scaled insurance predictive modeling applications, due to inefficiencies of Bayesian estimation procedures. The paper presents an efficient method that parallelizes Bayesian computation using distributed computing on Apache Spark across a cluster of computers. The distributed algorithm dramatically boosts the speed of Bayesian computation and expands the scope of applicability of Bayesian methods in insurance modeling. The empirical analysis applies a Bayesian hierarchical Tweedie model to a big data of 13 million insurance claim records. The distributed algorithm achieves as much as 65 times performance gain over the non-parallel method in this application. The analysis demonstrates that Bayesian methods can be of great value to large-scaled insurance predictive modeling.
Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 1107029872
Category : Business & Economics
Languages : en
Pages : 565
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Book Description
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.
Author: Michel Denuit
Publisher: Springer Nature
ISBN: 3030258270
Category : Business & Economics
Languages : en
Pages : 250
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Book Description
This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible. Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. This is the third 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: Jodi Lynn Palm
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 104
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Book Description
Author: Arthur Charpentier
Publisher: CRC Press
ISBN: 1466592591
Category : Business & Economics
Languages : en
Pages : 652
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Book Description
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).
Author: Marco Corazza
Publisher: Springer
ISBN: 3319898248
Category : Business & Economics
Languages : en
Pages : 465
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Book Description
The interaction between mathematicians, statisticians and econometricians working in actuarial sciences and finance is producing numerous meaningful scientific results. This volume introduces new ideas, in the form of four-page papers, presented at the international conference Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), held at Universidad Carlos III de Madrid (Spain), 4th-6th April 2018. The book covers a wide variety of subjects in actuarial science and financial fields, all discussed in the context of the cooperation between the three quantitative approaches. The topics include: actuarial models; analysis of high frequency financial data; behavioural finance; carbon and green finance; credit risk methods and models; dynamic optimization in finance; financial econometrics; forecasting of dynamical actuarial and financial phenomena; fund performance evaluation; insurance portfolio risk analysis; interest rate models; longevity risk; machine learning and soft-computing in finance; management in insurance business; models and methods for financial time series analysis, models for financial derivatives; multivariate techniques for financial markets analysis; optimization in insurance; pricing; probability in actuarial sciences, insurance and finance; real world finance; risk management; solvency analysis; sovereign risk; static and dynamic portfolio selection and management; trading systems. This book is a valuable resource for academics, PhD students, practitioners, professionals and researchers, and is also of interest to other readers with quantitative background knowledge.
