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[CFA] ASM Study Manual for SOA C CAS 4 14th edition - Third Printing   [推广有奖]

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ASM Study Manual Exam C/Exam 4, 14th Edition 3rd Printing
sample免费下载
http://www.docin.com/p-526313150.html

This manual offers comprehensive coverage of the syllabus for SOA Exam C and CAS Exam 4. In its 59 lessons, the following topics are reviewed:
. Parametric distributions
. Risk measures
. Coverage modifications, including deductibles, claims limits, coinsurance, and inflation
. Discrete distributions
. Compound models
. Aggregate deductibles
. Empirical estimators, including ogives, product-limit and Nelson-Aalen
. Kernel smoothing
. Parametric estimation, including method of moments, percentile matching, and maximum likelihood
. Hypothesis testing
. Limited fluctuation credibility
. Bayesian credibility
. Buhlmann credibility
. Empirical Bayes methods
. Simulation using the inversion method
. Bootstrap approximation

The manual has about 1500 pages, about 250 examples, 72 in-lesson excercises, and about 1300 end-of-lesson exercises, including both original and old exam questions, many of them taken from pre-2000 exams, which are not easily available, all with worked out solutions. In addition to these exercises, there are 13 full-length (35-question) practice exams. An appendix has a cross reference to help you track down lessons corresponding to published 2000 and later exam questions, and there is an index. Many students have found this manual adequate by itself for exam preparation, but the Klugman textbook for the exam, as well as the other text options where available, are referenced in each lesson if you wish to use them.

New for Spring 2013
Corrections of all errata through the date of publication have been incorporated. The manual now also lists the corresponding sections in Loss Models, 4th edition (as well as the 3rd edition) for each lesson.


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关键词:Study Manual printing Edition Manual editio following including measures manual limits

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林小姐 发表于 2012-11-17 22:41:28 |只看作者 |坛友微信交流群
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林小姐 发表于 2012-11-17 22:41
谢谢
呵呵,最近复习的怎么样?
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kan kan

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