Paperback, 312 Pages
Published: JUN-2012
ISBN 13: 978-0-12-386943-2
What would you like to do with your life? What career would allow you to fulfill your dreams of success? If you like mathematics-and the prospect of a highly mobile, international profession-consider becoming an actuary.
Szabo’s Actuaries’ Survival Guide, Second Edition explains what actuaries are, what they do, and where they do it. It describes exciting combinations of ideas, techniques, and skills involved in the day-to-day work of actuaries. This second edition has been updated to reflect the rise of social networking and the internet, the progress toward a global knowledge-based economy, and the global expansion of the actuarial field that has occurred since the first edition.
Actuarial mathematics students, career-minded students in courses such as macroeconomics, microeconomics, finance, and business, and for beginning professionals who are interested in career alternatives
Preface
1. Actuarial Careers
2. Actuarial Education
3. Actuarial Jobs
A. Consulting Firms
B. Insurance Companies
C. Reciprocity Agreements
D. Actuarial Websites
E. Recruiting Agencies
F. SOA Education Summary
G. CAS Education Summary
H.Actuarial Symbols
I. Bibliography
Hardbound, 300 Pages
Published: DEC-2012
ISBN 13: 978-0-12-396963-7
1.Introduction
1.Introduction, Data mining
1.R
2.Datasets used in this book
2.Data Loading and Exploration
1.Data Import/Export
1.Save/Load R Data
2.Import from and Export to .CSV Files
3.Import Data from SAS
4.Import/Export via ODBC
2.Data Exploration
1.Have a Look at Data
2.Explore Individual Variables
3.Explore Multiple Variables
4.More Exploration
5.Save Charts as Files
3.Data Mining Examples
1.Decision Trees
1.Building Decision Trees with Package party
2.Building Decision Trees with Package rpart
3.Random Forest
2.Regression
1.Linear Regression
2.Logistic Regression
3.Generalized Linear Regression
4.Non-linear Regression
3.Clustering
1.K-means Clustering
2.Hierarchical Clustering
3.Density-based Clustering
4.Outlier Detection
5.Time Series Analysis
1.Time Series Decomposition
2.Time Series Forecast
6.Association Rules
7.Sequential Patterns
8.Text Mining
9.Social Network Analysis
4.Case Studies
1.Case Study I: Analysis and Forecasting of House Price Indices
1.Reading Data from a CSV File
2.Data Exploration
3.Time Series Decomposition
4.Time Series Forecasting
5.Discussion
2.Case Study II: Customer Response Prediction
3.Case Study III: Risk Rating using Decision Tree with Limited Resources
4.Customer Behaviour Prediction and Intervention
5.Appendix
1.Online Resources
2.R Reference Card for Data Mining
Hardback
Series: Cambridge Tracts in Mathematics(No. 192)
ISBN:9781107017771
70 exercises
Dimensions: 228 x 152 mm
available from April 2012
Stein's method is a collection of probabilistic techniques that allow one to assess the distance between two probability distributions by means of differential operators. In 2007, the authors discovered that one can combine Stein's method with the powerful Malliavin calculus of variations, in order to deduce quantitative central limit theorems involving functionals of general Gaussian fields. This book provides an ideal introduction both to Stein's method and Malliavin calculus, from the standpoint of normal approximations on a Gaussian space. Many recent developments and applications are studied in detail, for instance: fourth moment theorems on the Wiener chaos, density estimates, Breuer?Major theorems for fractional processes, recursive cumulant computations, optimal rates and universality results for homogeneous sums. Largely self-contained, the book is perfect for self-study. It will appeal to researchers and graduate students in probability and statistics, especially those who wish to understand the connections between Stein's method and Malliavin calculus.
Preface
Introduction
1. Malliavin operators in the one-dimensional case
2. Malliavin operators and isonormal Gaussian processes
3. Stein's method for one-dimensional normal approximations
4. Multidimensional Stein's method
5. Stein meets Malliavin: univariate normal approximations
6. Multivariate normal approximations
7. Exploring the Breuer?Major Theorem
8. Computation of cumulants
9. Exact asymptotics and optimal rates
10. Density estimates
11. Homogeneous sums and universality
Appendix 1. Gaussian elements, cumulants and Edgeworth expansions
Appendix 2. Hilbert space notation
Appendix 3. Distances between probability measures
Appendix 4. Fractional Brownian motion
Appendix 5. Some results from functional analysis
References
Index.
Hardback
Series: Cambridge Series in Statistical and Probabilistic Mathematics(No. 36)
ISBN:9780521862141
200 b/w illus. 400 tables 80 exercises
Dimensions: 253 x 177 mm
available from August 2012
This book is about the statistical principles behind the design of effective experiments and focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis. Emphasising the logical principles of statistical design, rather than mathematical calculation, the authors demonstrate how all available information can be used to extract the clearest answers to many questions. The principles are illustrated with a wide range of examples drawn from real experiments in medicine, industry, agriculture and many experimental disciplines. Numerous exercises are given to help the reader practise techniques and to appreciate the difference that good design can make to an experimental research project. Based on Roger Mead's excellent Design of Experiments, this new edition is thoroughly revised and updated to include modern methods relevant to applications in industry, engineering and modern biology. It also contains seven new chapters on contemporary topics, including restricted randomisation and fractional replication.
1. Introduction
2. Elementary ideas of blocking: the randomised complete block design
3. Elementary ideas of treatment structure
4. General principles of linear models for the analysis of experimental data
5. Experimental units
6. Replication
7. Blocking and control
8. Multiple blocking systems and crossover designs
9. Multiple levels of information
10. Randomisation
11. Restricted randomisation
12. Experimental objectives, treatments and treatment structures
13. Factorial structure and particular forms of effects
14. Fractional replication
15. Incomplete block size for factorial experiments
16. Quantitative factors and response functions
17. Multifactorial designs for quantitative factors
18. Split unit designs
19. Multiple experiments and new variation
20. Sequential aspects of experiments and experimental programmes
21. Designing useful experiments.