Tag Archives: Statistical computing

PMean: Using statistical design principles to plan a Monte Carlo analysis – part 2

I’ve been working more on a Monte Carlo study of various Bayesian estimators and it makes me think about certain principles that we statisticians use in experimental design that could help us not just with other people’s laboratory studies, but with Monte Carlo studies, which are our own laboratories. This is a continuation of an earlier blog post. One important principle is variable transformation. We almost always conceptualize and analyze proportions using the logit transformation, and this transformation can help a lot with Monte Carlo studies as well. Continue reading

PMean: Using statistical design principles to plan a Monte Carlo analysis

I want to run a Monte Carlo analysis of various Bayesian estimators to see how they perform when the prior distribution is “wrong”. I’m like everyone else–I just plunge in and start. But halfway through the Monte Carlo analysis, I realized that I could make my life easier and produce a better quality Monte Carlo analysis if I used basic statistical design principles. Here’s a brief outline of some of these design principles. Continue reading