Statistical and Probabilistic Methods in Actuarial Science

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A01=Philip J. Boland
actuarial risk modelling techniques
actuarial science
Adjustment Coefficient
Aggregate Claims
Aggregate Claims Process
Author_Philip J. Boland
Category=KFFN
Chain Ladder Method
Claim Size
Claim Size Distribution
Claim Size Random Variable
Collective Risk Model
Compound Binomial Distribution
Compound Poisson Approximation
Compound Poisson Distribution
credibility estimation
D1 D2 D3 D4
Development Year
Discount Class
Discount Level
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Individual Risk Model
insurance mathematics
linear models
loss distributions
Loss Reinsurance
Markov chain applications
Minimax Criterion
motor insurance
Origin Year
Payoff Matrix
Philip J. Boland
Poisson Parameter
Proportional Reinsurance
Pure Premium
Risk theory
ruin probability
Security Loading
statistical software R
stochastic modelling
Surplus Process

Product details

  • ISBN 9781584886952
  • Weight: 780g
  • Dimensions: 156 x 234mm
  • Publication Date: 05 Mar 2007
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

University College Dublin, Ireland

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