I would like to use
rstanarm::stan_gamm4 for a study we’re preparing, and to substantiate my choice of Bayesian parameter estimation (unfortunately, this is still needed) I’ve come across some these claims and was wondering if there were any references to back them:
- “The documentation of lme4 and gamm4 has various warnings that acknowledge that the estimated standard errors, confidence intervals, etc. are not entirely correct, even from a frequentist perspective.
A frequentist point estimate would also completely miss the second mode in the last example with stan_nlmer. Thus, there is considerable reason to prefer the rstanarm variants of these functions for regression modeling. The only disadvantage is the execution time required to produce an answer that properly captures the uncertainty in the estimates of complicated models such as these.” (Conclusion in link)
- “Estimating these models via MCMC avoids the optimization issues that often crop up with GAMMs and provides better estimates for the uncertainty in the parameter estimates.” (last sentence of first paragraph of the Details in the documentation)
If anyone has such references, examples or similar, I’d be keen to know of their existence.