A picture may be worth a thousand words, but whilst many of us may get a pre-attentive gut reaction reading from a data set visualised using a chart type we’re familiar with, how many of us actually take the time to read a chart thoroughly and maybe verbalise, even ... [Read more...]

Email I was recently at a presentation where the success rates for EPSRC fellowships were given by theme. The message of the talk was that Engineering fellowships were under-subscribed and so we should all be preparing our applications. But just because a theme is under-subcribed doesn’t mean that you’... [Read more...]

Comparing the behavior of the two on the S&P 500. Previously There have been a few posts about Value at Risk (VaR) and Expected Shortfall (ES) including an introduction to Value at Risk and Expected Shortfall. Data and model The underlying data are daily returns for the S&P 500 from 1950 ... [Read more...]

Contingency tables are a very good way to summarize discrete data.  They are quite easy to construct and reasonably easy to understand. However, there are many nuances with tables and care should be taken when making conclusions related to the data. Here are just a few thoughts on the topic. ... [Read more...]

This past August I took an opportunity to step back into the University academic world as a Visiting Assistant Professor at the University of Arkansas. I have enjoyed the transition back into the academic world, including a more flexible schedule, variation in topics/duties, and collaborating with colleagues. However, there ... [Read more...]

american children of the nineties might have had pogs, beanie babies, m.c. hammer, but we lacked a reliable source for state-level survey estimates on health.  then in 2003, the maternal and child health bureau of the health services and resources... [Read more...]

ShinyApps.io is a new server which is currently in alpha testing to host Shiny applications.  It is being designed by the RStudio team and provides some distinct features different from that of the ShinyApps.io is intended for larger applications ... [Read more...]

Last week I dabbled in building classification trees with the party and rpart packages.  Now, I want to put together a series where I can apply those basic trees along with advanced techniques like bagging, boosting and random forest.  Additi... [Read more...]

The SHELDUS database is database on human and property losses from natural disasters in the United States. Although the data is free, downloading the data is tedious and so is cleaning and analyzing it. The new R package sheldusr comes w... [Read more...]

Someone asked about this and I though the solution with ggplot2 was pretty neat. Imagine that you have a scatterplot with some points in the exact same coordinates, and to reduce overplotting you want to have the size of the dot indicating the number of data points that fall on ... [Read more...]

Bayesian methods are sure to get some publicity after Vale Johnson’s PNAS paper regarding the use of Bayesian approaches to recalibrate p-value cutoffs from 0.05 to 0.005. Though the paper itself is bound to get some heat (see the discussion in Andrew Gelman’s blog and Matt Briggs’s fun-to-read deconstruction), ... [Read more...]

In this short post, we give the code snippets for both the least-square method (LS) and the maximum likelihood estimation (MLE). They are based on Calibrating the Ornstein-Uhlenbeck (Vasicek) model at www.sitmo.com. One can also read the article On the Simulation and Estimation … Continue reading → [Read more...]

After reading Blog About Stats' Open Data Index Blog Post I decided to browse a bit in the Open Data Index. Choosing Netherlands and following Emission of Pollutants I ended on a page from National Institute for Public Health. The page&n... [Read more...]

Welcome to the second part! In previous part, we understood Linear regression, cost function and gradient descent. In this part we will implement whole process in R step by step using example data set. I will use the data set provided in the machine learning class assignment. We will implement ... [Read more...]

Welcome to the first part of my series blog post. In this post, I will discuss about how to implement linear regression step by step in R by understanding the concept of regression. I will try to explain the concept of linear regression in very short manner and try to ... [Read more...]

As I was responding to the points raised by two referees and the editor on my paper on cost-effectiveness with structural zeros (the preliminary version was here, while I have presenting it in a few talks and discussed it here, here, here and here... [Read more...]

This post demonstrates the dynamics involved in a susceptible, infected, and recovering (SIR) model previous post for the model.  The shiny ui and server code can be found on GitHub.of dynamic programming. As a dynamic infection model, I find it p... [Read more...]

These are the slides from my talk at FP Days 2013. The first part goes into more detail about why …Continue reading » [Read more...]

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Last month I joined Gregory Piatetsky (KDnuggets editor) for a webinar presentation Data Science: Not Just for Big Data, hosted by Kalido. In my portion of the prentation (you can see my slides below), I wanted to react to the Big Data focus which is so much a part of ... [Read more...]