A couple of weeks ago, I had a discussion with a practitioner, working in some financial company, about ruin, and infinite time. And it remind me a weird result. Well, not a weird result, but a result I found disturbing, at first, when I was a stud...
Check them out. Here are thirty homoskedastic ones: > homo.wiener for (j in 1:30) { for (i in 2:length(homo.wiener)) { homo.wiener for (j in 1:30) { plot( homo.wiener, type = "l", col = rgb(.1,....
Check them out. Here are thirty homoskedastic ones: > homo.wiener for (j in 1:30) { for (i in 2:length(homo.wiener)) { homo.wiener for (j in 1:30) { plot( homo.wiener, type = "l", col = rgb(.1,....
We’re really interested in markets, but we’ll start with a series of coin tosses. If the coin lands heads, then we go up one; if it lands tails, we go down one. Figure 1: A coin toss path.Figure 1 is the result of one thousand coin flips. It is a random walk. The R command … Continue reading...
In the previous post (here) discussing forecasts of actuarial quantities, I did not mention much how to forecast the temporal component in the Lee-Carter model. Actually, many things can be done. Consider here some exponential smoothing techniques ...
A new Metropolis-Hastings algorithm that I would call “universal” was posted by Somak Dutta yesterday on arXiv. Multiplicative random walk Metropolis-Hastings on the real line contains a different Metropolis-Hastings algorithm called the random dive. The proposed new value x’ given the current value x is defined by when is a random variable on . Thus, 
Matt Asher posted an R experiment on R-bloggers yesterday simulating the random walk which has the property of avoiding zero by quickly switching to a large value as soon as is small. He was then wondering about the “convergence” of the random walk given that it moves very little once is large enough. The values 
If you are used to object oriented programing in a different language, the way R does things can seem a little strange and backwards. “proto” to the rescue. With this library you can simulate “normal” OOP. I found the examples for proto not so helpful, so to figure out how the package works I sent
Following my remarks on the t-walk algorithm in the recent A General Purpose Sampling Algorithm for Continuous Distributions, published by Christen and Fox in Bayesian Analysis that acts like a general purpose MCMC algorithm, Darren Wraith tested it on the generic (10 dimension) banana target we used in the cosmology paper. Here is an output 
When I read in the abstract of the recent A General Purpose Sampling Algorithm for Continuous Distributions, published by Christen and Fox in Bayesian Analysis that We develop a new general purpose MCMC sampler for arbitrary continuous distributions that requires no tuning. I am slightly bemused. The proposal of the authors is certainly interesting and 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
