5
$\begingroup$

I doing spatial bayesian data analysis, I am assuming a no-nugget exponential covariance. I have tried a variety of priors for the sills and range parameters (gamma, inverse gamma etc.) , unfortunately the convergence diagonstics are typically horrible.

I am wondering how to figure out the poor mixing I observe, is there something I can do to make the MCMC chain behave better?

Improve this question
$\endgroup$

1 Answer 1

Reset to default
4
$\begingroup$

Diggle and Ribeiro discuss this in their book ("Model-based Geostatistics"): see section 5.4.2. They quote some research suggesting that re-parameterization might help a little. For an exponential model (a Matern model with kappa = 1/2) this research suggests using the equivalent of log(sill/range) and log(range). Diggle and Ribeiro themselves recommend a profile likelihood method to investigate the log-likelihood surface. Their software is implemented in the R package geoRglm.

Have you looked at an experimental variogram to check that a zero nugget and an exponential shape are appropriate?

Improve this answer
$\endgroup$
2
  • $\begingroup$ Thanks. I fitted a no nugget model to the likelihood using proc mixed, I guess I should compare the AICC with that of a model with nugget. $\endgroup$
    – Arin Chaudhuri
    Commented Aug 19, 2010 at 18:08
  • $\begingroup$ I would agree with whuber though that the visualization of the variogram cloud would be really helpful in determining if you choice of model seems appropriate. $\endgroup$
    – TheSteve0
    Commented Nov 28, 2010 at 7:51

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.