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’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...

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

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