Organisation : Casualty Actuarial Society
Name Of The Exam : Modern Actuarial Statistics – II MAS
Announcement : Syllabus
Home Page : http://www.casact.org/
Download Syllabus : http://www.syllabus.gen.in/uploads/916-MAS-II.pdf
MAS II Exam Syllabus :
Learning Objectives : set forth, usually in broad terms, what the candidate should be able to do in actual practice.
Related : MAS-I Exam Syllabus Modern Actuarial Statistics Casualty Actuarial Society : www.syllabus.gen.in/913.html
Included in these learning objectives are certain methodologies that may not be possible to perform on an examination, such as applying the Metropolis-Hastings algorithm or building a decision tree, but that the candidate would still be expected to explain conceptually in the context of an examination.
Knowledge Statements : identify some of the key terms, concepts, and methods that are associated with each learning objective.
These knowledge statements are not intended to represent an exhaustive list of topics that may be tested, but they are illustrative of the scope of each learning objective.
Readings : support the learning objectives. It is intended that the readings, in conjunction with the material on earlier examinations, provide sufficient resources to allow the candidate to perform the learning objectives.
Some readings are cited for more than one learning objective. The CAS Syllabus & Examination Committee emphasizes that candidates are expected to use the readings cited in this Syllabus as their primary study materials.
Thus, the learning objectives, knowledge statements, and readings complement each other.
The learning objectives define the behaviors, the knowledge statements illustrate more fully the intended scope of the learning objectives, and the readings provide the source material to achieve the learning objectives.
Learning objectives should not be seen as independent units, but as building blocks for the understanding and integration of important competencies that the candidate will be able to demonstrate.
Note that the range of weights shown should be viewed as a guideline only. There is no intent that they be strictly adhered to on any given examination—the actual weight may fall outside the published range on any particular examination.
A. Introduction to Credibility :
Range of weight for Section A : 5-15 percent
Advances in statistical computing tools have now made it practical to include a form of credibility weighting when building regression type models.
For example, what the statisticians call shrinkage in a Linear Mixed Effect Model is a form of least squares credibility weighting. These advanced techniques are covered extensively in Sections B and C.
However, candidates should be familiar with the topics listed below as they can serve as a good introduction to those techniques and are still very much in practice today.
Specifically, candidates should be familiar with limited fluctuation credibility and be able to calculate estimates using Bayesian credibility procedures.
They should also be fluent with Bayesian and Bühlmann (least squares credibility) procedures both for discrete and continuous models
Learning Objectives :
1. Understand the basic framework of credibility and be familiar with limited fluctuation credibility, including partial and full credibility
2. Understand the basic framework of Bühlmann credibility
3. Calculate different variance components for Bühlmann credibility
4. Calculate Bühlmann and Bühlmann-Straub credibility factor and estimates for frequency, severity, and aggregate loss
5. Understand the basic framework of Bayesian credibility
6. Calculate Bayes estimate/Bayesian premium
7. Bayesian versus Bühlmann credibility for conjugate distributions
8. Calculate credibility estimates using the Nonparametric empirical Bayes Method
Knowledge Statements :
a. Limited fluctuation credibility, Partial and Full Credibility
b. Conjugate priors, Poison/Gamma, Binomial/Beta, Normal/Normal
c. Bühlmann Credibility Continuous
d. Bühlmann Credibility Discrete
e. Bayesian Analysis Discrete
f. Bayesian Analysis Continuous
g. Nonparametric Empirical Bayes