I am finding myself more and more drawn to markdown rather then tex/Rnw as my standard format (not least of which is the ease of displaying the files on github, particularly now that we have automatic image uploading). One thing I miss from latex is the citation commands. (I understand these can be provided to
David Varadi have recently wrote two posts about Gini Coefficient: I Dream of Gini, and Mean-Gini Optimization. I want to show how to use Gini risk measure to construct efficient frontier and compare it with alternative risk measures I discussed previously. I will use Gini mean difference risk measure – the mean of the difference
I was recently asked to summarise an analysis using a ROC (Receiver-operator characteristics) plot. R has a number of particularly good tools to produce ROC plots – ROCR, pROC and the Bioconductor package ROC to name a few. However I thought it would be a useful exercise
I’m not always careful in citing all the R packages I use. R actually has some rather nice built-in mechanisms to support this, so I really have no excuse. Here’s some quick examples: To cite the ouch package in publications use: Aaron A. King and Marguerite A. Butler (2009), ouch: Ornstein-Uhlenbeck models for phylogenetic comparative
In the last post, Portfolio Optimization: Specify constraints with GNU MathProg language, Paolo and MC raised a question: “How would you construct an equal risk contribution portfolio?” Unfortunately, this problem cannot be expressed as a Linear or Quadratic Programming problem. The outline for this post: I will show how Equal Risk Contribution portfolio can be
In Chapter 2 (Confidence Intervals) of Serious stats I consider the problem of displaying confidence intervals (CIs) of a set of means (which I illustrate with the simple case of two independent means). Later, in Chapter 16 (Repeated Measures ANOVA), I consider the trickier problem of displaying of two or more means from paired or
I was writing comments on the blog post A proposal for a really fast statistics journal, and I realized the comment box was too small to write down my ideas. I like the proposal a lot, and I feel really bad about the current model of submitting and rev...
If I google for “probability distribution” I find the following extremely bad picture: It’s bad because it conflates ideas and oversimplifies how variable probability distributions can generally be. Most distributions are not unimodal. Most dist...
While playing around with Bayesian methods for random effects models, it occured to me that inverse-Wishart priors can really bite you in the bum. Inverse Wishart-priors are popular priors over covariance functions. People like them priors because they are conjugate to a Gaussian likelihood, i.e, if you have data with each : so that the