We've seen in the previous post (here) how important the *-cartesian product to model joint effected in the regression. Consider the case of two explanatory variates, one continuous (, the age of the driver) and one qualitative (, gasoline ve...

Edward Kao just sent another typo found both in Monte Carlo Statistical Methods (Problem 3.21) and in Introducing Monte Carlo Methods with R (Exercise 3.17), namely that should be I also got another email from Jerry Sin mentioning that matrix summation in the matrix commands of Figure 1.2 of Introducing Monte Carlo Methods with R

There is often confusion about how to include covariates in ARIMA models, and the presentation of the subject in various textbooks and in R help files has not helped the confusion. So I thought I’d give my take on the issue. To keep it simple, I will only describe non-seasonal ARIMA models although the ideas

Edward Kao pointed out several typos in Example 5.18 of Monte Carlo Statistical Methods. First, the customers in area i should be double-indexed, i.e. which implies in turn that . Then the summary T should be defined as and as given that the first m customers have the fifth plan missing. Filed under: Books, R,

As I am working on my dissertation and piecing together a mess of notes, code and output, I am wondering to myself “how long is this thing supposed to be?” I am definitely not into this to win the prize for longest dissertation. I just want to say my piece, make my point and move on. I’ve heard that...

Stephen Stigler has written a paper in the Journal of the Royal Statistical Society Series A on Francis Galton’s analysis of (his cousin) Charles Darwin’ Origin of Species, leading to nothing less than Bayesian analysis and accept-reject algorithms! “On September 10th, 1885, Francis Galton ushered in a new era of Statistical Enlightenment with an address

Here is a (prompt!) reply from Mark Girolami corresponding to the earlier post: In preparation for the Read Paper session next month at the RSS, our research group at CREST has collectively read the Girolami and Calderhead paper on Riemann manifold Langevin and Hamiltonian Monte Carlo methods and I hope we will again produce a

In the previous days I have received several emails asking for clarification of the effective sample size derivation in “Introducing Monte Carlo Methods with R” (Section 4.4, pp. 98-100). Formula (4.3) gives the Monte Carlo estimate of the variance of a self-normalised importance sampling estimator (note the change from the original version in Introducing Monte

One of the challenges for ecologists working with trophic/interaction networks is to understand their organization. One of the possible approaches is to compare them across a random model, with more or less constraints, in order to estimate the departure from randomness. To this effect, null models have been developed. The basic idea behind a null