ABSTRACT
Many of the methods provided in other chapters of this book offer solutions for samples that are small in an absolute sense; for example, in single-case designs. In this chapter, the focus is instead on small samples relative to the complexity of the model. I illustrate how Bayesian penalization offers a solution to this problem by applying so-called “shrinkage priors” that shrink small effects towards zero while leaving substantial effects large. A tutorial is provided on applying Bayesian penalization to a linear regression model using the R package bayesreg, which implements various shrinkage priors.
