R code for Chapter 2 of Non-Life Insurance Pricing with GLM

13 March 2012

We continue working our way through the examples, case studies, and exercises of what is affectionately known here as “the two bears book” (Swedish björn = bear) and more formally as Non-Life Insurance Pricing with Generalized Linear Models by Esbjörn Ohlsson and Börn Johansson (Amazon UK | US).

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R code for Chapter 1 of Non-Life Insurance Pricing with GLM

1 March 2012

Insurance pricing is backwards and primitive, harking back to an era before computers. One standard (and good) textbook on the topic is Non-Life Insurance Pricing with Generalized Linear Models by Esbjorn Ohlsson and Born Johansson (Amazon UK | US). We have been doing some work in this area recently. Needing a robust internal training course and documented methodology, we have been working our way through the book again and converting the examples and exercises to R, the statistical computing and analysis platform. This is part of a series of posts containing elements of the R code.

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R versus SAS/SPSS in corporations

28 October 2011

Background

We have created and managed analytics teams in commercial organizations (mainly telecommunications) across Europe. The teams were using SAS or SPSS. Our company now has a commercial analytics as a service offering and we mainly use R.

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Commercial Analytics: The Capabilities

5 October 2011

Commercial Analytics is the kind that makes money. From data to dollars, insights to income, this is all about how to run the business better. To do it and to do it well you need certain capabilities in place. This article builds a map of those business capabilities to help you assess, understand, and plan your business.

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5 common pitfalls of commercial analytics projects

5 September 2011

We have 20 years of experience of big data environments within a variety of industries including Research, Banking, Insurance, and Telecommunications. We have especially worked with customer data: Marketing, Risk Management, customer segmentation and -profitability, and customer-driven product development.

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Friday quote: what is the question to which this number is the answer?

26 August 2011

John Kay muses on interpreting statistical data:

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A warning on the R save format

23 August 2011

The save() function in the R platform for statistical computing is very convenient and I suspect many of us use it a lot. But I was recently bitten by a “feature” of the format which meant I could not recover my data.

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Friday quote: the handmaiden and the whore

19 August 2011

Because it is Friday and because we collect quotes, here is one on statistics being the best and worst of disciplines. Which one of the two views are closest to your opinion?

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Spreadsheet errors

20 April 2011

For my sins, I have done more than my fair share of analysis in Excel. I am quite capable of building and maintaining 130Mb spreadsheets (I had a dozen of them for one client). Excel is pretty much installed everywhere, so it is sometimes the only way to get started getting commercial value of the data in the organisation. But I don’t like it and let’s have a look at one reason why. In order not to always pick on Microsoft, we use another application, but you get the same results with Excel.

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Getting started with the Heritage Health Price competition

8 April 2011

The US$ 3 million Heritage Health Price competition is on so we take a look at how to get started using the R statistical computing and analysis platform.

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