NAG on the Cloud: Part 2

In case you missed Part 1 of NAG on the Cloud, I discussed calling the Library on Windows Azure using the C# library and Microsoft's Cloud Numerics. We now turn to a different cloud provider. Amazon's Elastic Cloud Computing (EC2) allows the user to buy or rent instances of virtual computers and pay for only what you use. Below, I'll discuss how to put the NAG Library on EC2 and give some initial performance tests.

Creating an Instance 

There are a plethora of videos on creating Amazon EC2 instances. If you are going to install and run the NAG Library, make sure you start a Virtual Private Cloud when creating the instance. Private Clouds can be created at no additional cost on EC2 and remain compatible with NAG's Kusari License management system. I decided to create a couple tests:

My EC2 Console Instances

The above instances vary in size, memory, and type of OS running. When the instance is created, you can gain access to it via ssh. To load the library onto EC2, just download the .tgz file of your choice from the NAG website and secure copy it up there:

$scp -i VPCkey.pem fll6a23dfl.tgz ubuntu@50.112.131.89:/home/ubuntu

Here VPCkey.pem is a key file that only allows my computer to access this Cloud instance. Once the Library is copied, we can unzip it and the installation instructions remain the same as if the computer were right in front of you!

What's new at Mark 23? Linear quantile regression

This is the first in an occasional series of posts highlighting new functionality in the latest release (Mark 23) of the NAG Library.  Here, we describe linear quantile regression, which has just been added to the collection of regression techniques that is already available in the Library.

Regression techniques are concerned with modelling and analyzing the relationship between a dependent (or response) variable and one or more independent (or explanatory) variables.  More specifically, they enable the user to understand how the typical value of the response variable changes when one or more of the explanatory variables are varied.  A common example of regression analysis is linear least-squares regression, which is concerned with modelling the behaviour of the conditional mean of the response variable and which, as its name implies, employs the well-known method of least-squares.  By contrast, linear quantile regression models one of the conditional quantiles of the response variable (see this note for more information on its formal definition).  The solution of this problem is obtained using linear programming techniques; least-squares methods cannot be used here.