Loss Models From Data to Decisions

ISBN-10: 0470187816
ISBN-13: 9780470187814
Edition: 3rd 2008
List price: $164.00
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Description: The contents of this online, instructor-driven courseware product parallel that of Exam C (old Exam 4) of the Society of Actuaries, the Casualty Actuarial Society and the Canadian Institute of Actuaries' combined accreditation programs. Full text  More...

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Book details

List price: $164.00
Edition: 3rd
Copyright year: 2008
Publisher: John Wiley & Sons, Incorporated
Publication date: 9/9/2008
Binding: Hardcover
Pages: 760
Size: 7.25" wide x 10.25" long x 2.00" tall
Weight: 3.696
Language: English

The contents of this online, instructor-driven courseware product parallel that of Exam C (old Exam 4) of the Society of Actuaries, the Casualty Actuarial Society and the Canadian Institute of Actuaries' combined accreditation programs. Full text with searchable links; scores of simulations and animations; accommodations for a community bulletin board; more than 75 plugged-in data sets (in EXCEL); thousands of uniquely designed and randomly selected sample test exercises, complete with hints and worked-out solutions; multiple forms of timed exams; and a built-in record-keeping/gradebook system (for both students and instructors) are all available in this version of the product, specifically designed as an alternative to in-classroom use.

Preface
Introduction
Modeling
The model-based approach
Organization of this book
Random variables
Introduction
Key functions and four models
Basic distributional quantities
Moments
Quantiles
Generating functions and sums of random variables
Tails of distributions
Measures of Risk
Actuarial Models
Characteristics of actuarial models
Introduction
The role of parameters
Continuous models
Introduction
Creating new distributions
Selected distributions and their relationships
The linear exponential family
TVaR for continuous distributions
Extreme value distributions
Discrete distributions and processes
Introduction
The Poisson distribution
The negative binomial distribution
The binomial distribution
The (a, b, 0) class
Counting processes
Truncation and modification at zero
Compound frequency models
Further properties of the compound Poisson class
Mixed Poisson distributions
Mixed Poisson processes
Effect of exposure on frequency
An inventory of discrete distributions
TVaR for discrete distributions
Multivariate models
Introduction
Sklar�s theorem and copulas
Measures of dependency
Tail dependence
Archimedean copulas
Elliptical copulas
Extreme value copulas
Archimax copulas
Frequency and severity with coverage modifications
Introduction
Deductibles
The loss elimination ratio and the effect of inflation for ordinary deductibles
Policy limits
Coinsurance, deductibles, and limits
The impact of deductibles on claim frequency
Aggregate loss models
Introduction
Model choices
The compound model for aggregate claims
Analytic results
Computing the aggregate claims distribution
The recursive method
The impact of individual policy modifications on aggregate payments
Inversion methods
Calculations with approximate distributions
Comparison of methods
The individual risk model
TVaR for aggregate losses
Discrete-time ruin models
Introduction
Process models for insurance
Discrete, finite-time ruin probabilities
Continuous-time ruin models
Introduction
The adjustment coefficient and Lundberg�s inequality
An integrodifferential equation
The maximum aggregate loss
Cramer�s asymptotic ruin formula and Tijms' approximation
The Brownian motion risk process
Brownian motion and the probability of ruin
Construction Of Empirical Models
Review of mathematical statistics
Introduction
Point estimation
Interval estimation
Tests of hypotheses
Estimation for complete data
Introduction
The empirical distribution for complete, individual data
Empirical distributions for grouped data
Estimation for modified data
Point estimation
Means, variances, and interval estimation
Kernel density models
Approximations for large data sets
Parametric Statistical Methods
Parameter estimation
Method of moments and percentile matching
Maximum likelihood estimation
Variance and interval estimation
Non-normal confidence intervals
Bayesian estimation
Estimation for discrete distributions
Exercises
Model selection
Introduction
Representations of the data and model
Graphical comparison of the density and distribution functions
Hypothesis tests
Selecting a model
Estimation and model selection for more complex models
Extreme value models
Copula models
Models with covariates
Five examples
Introduction
Time to death
Time from incidence to report
Payment amount
An aggregate loss example
Another aggregate loss example
Comprehensive exercises
Adjusted Estimates
Interpolation and smoothing
Introduction
Polynomial interpolation and smoothing
Cubic spline interpolation
Approximating functions with splines
Extrapolating with splines
Smoothing splines
Credibility
Introduction
Limited fluctuation credibility theory
Greatest accuracy credibility theory
Empirical Bayes parameter estimation
Simulation
Simulation
Basics of simulation
Examples of simulation in actuarial modeling
Examples of simulation in finance
An inventory of continuous distributions
An inventory of discrete distributions
Frequency and severity relationships
The recursive formula
Discretization of the severity distribution
Numerical optimization and solution of systems of equations
References
Index

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