Loss Models From Data to Decisions

ISBN-10: 1118315324

ISBN-13: 9781118315323

Edition: 4th 2012

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Description:

This set contains 9780470385715 Loss Models: From Data to Decisions, Solutions Manual, 3rd Edition and 9780470308578 ExamPrep Online for Loss Models: From Data to Decisions, Online, 3rd Edition.To explore our additional offerings in actuarial exam preparation visit www.wiley.com/go/actuarialexamprep .
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Book details

List price: $154.95
Edition: 4th
Copyright year: 2012
Publisher: John Wiley & Sons, Incorporated
Publication date: 9/4/2012
Binding: Hardcover
Pages: 536
Size: 7.25" wide x 10.50" long x 1.25" tall
Weight: 3.278
Language: English

Preface
Introduction
Modeling
The model-based approach
Organization of this book
Random variables
Introduction
Key functions and four models
Basic distributional quantities
Moments
Percentiles
Generating functions and sums of random variables
Tails of distributions
Measures of Risk
Actuarial Models
Characteristics of Actuarial Models
Introduction
The role of parameters
Continuousmodels
Introduction
Creating new distributions
Selected distributions and their relationships
The linear exponential family
Discrete distributions
Introduction
The Poisson distribution
The negative binomial distribution
The binomial distribution
The (a, b, 0) class
Truncation and modification at zero
Advanced discrete distributions
Compound frequency distributions
Further properties of the compound Poisson class
Mixed frequency distributions
Effect of exposure on frequency
An inventory of discrete distributions
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
The individual risk model
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
Kernel density models
Approximations for large data sets
Parametric Statistical Methods
Frequentist estimation
Method of moments and percentile matching
Maximum likelihood estimation
Variance and interval estimation
Non-normal confidence intervals
Maximum likelihood estimation of decrement probabilities
Frequentist Estimation for discrete distributions
Poisson
Negative binomial
Binomial
The (a, b, 1) class
Compound models
Effect of exposure on maximum likelihood estimation
Exercises
Bayesian estimation
Definitions and Bayes' theorem
Inference and prediction
Conjugate prior distributions and the linear exponential family
Computational issues
Model selection
Introduction
Representations of the data and model
Graphical comparison of the density and distribution functions
Hypothesis tests
Selecting a model
Credibility
Introduction and Limited Fluctuation Credibility
Introduction
Limited fluctuation credibility theory
Full credibility
Partial credibility
Problems with the approach
Notes and References
Exercises
Greatest accuracy credibility
Introduction
Conditional distributions and expectation
The Bayesian methodology
The credibility premium
The Buhlmann model
The Buhlmann?Straub model
Exact credibility
Notes and References
Exercises
Empirical Bayes parameter estimation
Introduction
Nonparametric estimation
Semiparametric estimation
Notes and References
Exercises
Simulation
Simulation
Basics of simulation
Simulation for specific distributions
Determining the sample size
Examples of simulation in actuarial modeling
An inventory of continuous distributions
Introduction
Transformed beta family
Transformed gamma family
Distributions for large losses
Other distributions
Distributions with finite support
An inventory of discrete distributions
Introduction
The (a , b , 0) class
The (a , b , 1) class
The compound class
A hierarchy of discrete distributions
Frequency and severity relationships
The recursive formula
Discretization of the severity distribution
The method of rounding
Mean preserving
Undiscretization of a discretized distribution
Numerical optimization and solution of systems of equations
Maximization using Solver
The simplex method
Using Excel� to solve equations
References
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