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Product details
File Size: 47829 KB
Print Length: 563 pages
Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
Publisher: Cambridge University Press; 1 edition (July 28, 2014)
Publication Date: September 3, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00N4PLSO0
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Amazon Best Sellers Rank:
#394,417 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
This book has a wealth of information and, in general, is written with enough of both mathematical rigor and practical worked examples. Being a collection of almost independently written chapters, its exposition style not always internally consistent, however, if you are willing to overlook that and are willing to put some effort into understanding what is being said, you will find it quite useful. Refreshingly, it covers both traditional and Bayesian approach to regression modeling. Even though it is advertised as a reference for insurance professionals, the material covered applies to virtually any area of predictive analytics, including healthcare, market research, reliability and fraud analysis. Given its value to the practitioner, it is definitely a worthy purchase. I am looking forward to Volume II.
Data and associated R code is available at: http://instruction.bus.wisc.edu/jfrees/jfreesbooks/PredictiveModelingVol1/index.htm
very good book to "appetize" you for research
It is a gooD book introducinG many modern models and aproaches to everdyday problem of someone involved with insurance industries, they are self contained within each chapter so in case you need to read any particular approach to problem there is no problem, explanations are clear, do not expect something easy to understand because o this, the content is at a high mathematical level, but most of the techniques involve applications through software, especially R , so it is a good book on it's own
I hope you have better things to do with your life than reading this dreadfully organized book of boredom and pedantic nitty-grittiness.
Predictive modeling has become a hot topic in light of the new era of Big Data that is emerging. It occupies a unique position at the intersection of traditional statistical methods and machine learning algorithms. Practitioners of the art of model-building can benefit from the knowledge base provided by both disciplines. This excellent compendium focuses mainly on the statistical side of the equation. It offers comprehensive coverage of the relevant statistical techniques and can serve as a valuable reference and resource. The editors should be commended for the high quality of both the content and writing maintained throughout.Each chapter is almost a stand-alone mini-textbook on a particular method, and is introduced by a brief and informative summary. Each method is illustrated with well-developed insurance-related applications and R code for implementing the analyses. The presentation is sometimes at a fairly high mathematical level, but remains firmly moored to practical applications. Although the context is actuarial science, it may well be of interest to a wider audience.Perhaps the editors can be persuaded to undertake an equally impressive volume devoted to the machine-learning side of predictive modeling. Personally, I would like to see chapters dealing with various popular machine-learning techniques such as CART, Lasso, neural nets, genetic algorithms, KNN etc. Chapters on such supervised learning techniques would complement the excellent chapter by Louise Francis on unsupervised learning in the current volume. In any case, I look forward to the second planned companion volume that will present a wide range of case studies on actual projects.
I bought both the Kindle version and the hard cover. Below are my comments:Kindle version is difficult to follow since a lot of the mathematical notations show up in bigger fonts and in picture format than actual text. -- strongly not recommend buying the kindle versionThe book was written by multiple authors split up by chapter. Each chapter has examples. About 2/3 of the chapters either have no data or have dataset posted but without the R code to supplement it. When contacting authors for the R code for chapter examples, most of them are reluctant to share. Therefore, this book is not as useful as it seems.
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